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提交 34d6c085 authored 作者: 贾道铭's avatar 贾道铭
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detect.py
浏览文件 @ 34d6c085
......@@ -246,7 +246,7 @@ def parse_opt():
# 定义命令行可以传入的参数
parser = argparse.ArgumentParser()
# 权重
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/mask_new_300/weights/best.pt',
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp4/weights/best.pt',
help='model path or triton URL')
parser.add_argument('--source', type=str, default='owndata/test/video', help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
......
detect_new.py 0 → 100644
浏览文件 @ 34d6c085
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddle
"""
import argparse
import os
import platform
import sys
from pathlib import Path
from vidgear.gears import NetGear
import cv2
import torch
# 当前执行的detect.py的绝对路径，E:\Study\yolo\yolov5\detect.py
FILE = Path(__file__).resolve()
# parents[0]，获取detect.py的父路径，即E:\Study\yolo\yolov5
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path: # 模块的查询路径的列表
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative，绝对路径转换成相对路径
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode
@smart_inference_mode()
def run(
weights=ROOT / 'yolov5s.pt', # model path or triton URL
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
):
# 对传进的参数进行了一些判断操作
source = str(source) # 强制将路径转换为字符串类型， 'python detect.py --source data\\images\\bus.jpg
save_img = not nosave and not source.endswith('.txt') # save inference images
# 判断是不是文件地址,Path()查看文件的路径,suffix提取文件的后缀
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
# 判断是不是网络流地址
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
# 判断source参数是不是摄像头地址或者网络流地址
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
screenshot = source.lower().startswith('screen')
if is_url and is_file:
# 如果是网络流文件，则下载
source = check_file(source) # download
# Directories
# 新建保存结果的文件夹
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run, runs\\detect\\mask_300
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model,加载模型的权重
# 选择系统的设备，CPU/GPU
device = select_device(device)
# 通过框架(此处weights为.pt文件,所以是pytorch)加载模型
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
# 加载好模型中，读取模型的步长，类别名，是不是pytorch
stride, names, pt = model.stride, model.names, model.pt
# 图片size是不是满足32倍数
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
# 定义Dataloader模块，用来加载待预测的图片
bs = 1 # batch_size,每次输入1张图片
if webcam:
view_img = check_imshow(warn=True)
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
# 加载图片
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
# 执行模型的推理过程
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup翻译‘热身’，先给GPU随便传一张让他工作
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
# 进行图片预测
# im resize后的图片,im0s 原图,vid_cap None,s 打印信息
for path, im, im0s, vid_cap, s in dataset: # “E:\Study\yolo\yolov5\data\images\bus.jpg”
with dt[0]:
im = torch.from_numpy(im).to(model.device) # torch.Size([3, 640, 480]) from_numpy()将numpy格式的图片转换问tensor格式
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0 归一化操作
if len(im.shape) == 3:
im = im[None] # expand for batch dim , torch.Size([1, 3, 640, 480])
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment,
visualize=visualize) # 模型预测出来的所有的检测框 # torch.Size([1, 18900, 85]) yolov5预训练权重所输出的85个预测信息，4个坐标信息，1个置信度信息，80个分类
# NMS
with dt[2]:
# conf_thres 置信度阈值，iou_thres iou阈值， max_det 最大目标数
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms,
max_det=max_det) # 过滤后的信息 1, 5, 6 ,1个batch， 5个结果， 6中4个坐标信息，1个置信度信息，1个分类信息
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image , det 表示5个检测框的预测信息 torch.Size([5,6])
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
# 图片的保存路径
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
# s中加上图片size
s += '%gx%g ' % im.shape[2:] # print string
# 获得原图宽高大小
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
# 判断是否裁剪检测框的图片
imc = im0.copy() if save_crop else im0 # for save_crop
# 定义绘图工具，专门画框的
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
# 坐标映射，将640,480的图片上面宽的坐标映射到原图上
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(f'{txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Stream results
# 从annotator中返回画好框的图片
im0 = annotator.result()
if view_img:
if platform.system() == 'Linux' and p not in windows:
windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections) 保存图片
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
# Print results
# 最终打印出输出信息
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image 打印平均时间
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
class simulation_opt: # 参数对象。
def __init__(self, weights="runs/train/exp4/weights/best.pt", source=0, data="data/coco128.yaml", imgsz=[640, 640],
conf_thres=0.25, iou_thres=0.45, max_det=1000, device="", view_img=False, save_txt=False,
save_conf=False, save_crop=False, nosave=True, classes=None, agnostic_nms=False, augment=False,
visualize=False, update=False, project="runs/detect", name="exp", exist_ok=False, line_thickness=3,
hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1):
self.weights = weights
self.source = source
self.data = data
self.imgsz = imgsz
self.conf_thres = conf_thres
self.iou_thres = iou_thres
self.max_det = max_det
self.device = device
self.view_img = view_img
self.save_txt = save_txt
self.save_conf = save_conf
self.save_crop = save_crop
self.nosave = nosave
self.classes = classes
self.agnostic_nms = agnostic_nms
self.augment = augment
self.visualize = visualize
self.update = update
self.project = project
self.name = name
self.exist_ok = exist_ok
self.line_thickness = line_thickness
self.hide_labels = hide_labels
self.hide_conf = hide_conf
self.half = half
self.dnn = dnn
self.vid_stride = vid_stride
class detectapi:
def __init__(self, weights, source):
self.opt = simulation_opt(weights=weights, source=source)
print(vars(self.opt))
run(**vars(self.opt))
def parse_opt():
# 定义命令行可以传入的参数
parser = argparse.ArgumentParser()
# 权重
parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp4/weights/best.pt',
help='model path or triton URL')
parser.add_argument('--source', type=str, default=0, help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
#
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='show results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', default=True, help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--visualize', action='store_true', help='visualize features')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
# 打印所以的参数信息
# detect: weights=runs/train/exp4/weights/best.pt, source=owndata/test/video, data=data\coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs\detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
# YOLOv5 v7.0-112-g4751345d Python-3.7.16 torch-1.13.1 CPU
print_args(vars(opt))
return opt
# def main(opt):
# 检测包有没有成功安装
# check_requirements(exclude=('tensorboard', 'thop'))
# print(vars(opt))
# 执行run函数，并传入参数
# run(**vars(opt))
if __name__ == '__main__':
# receive_mode = True表示为接收数据
client = NetGear(receive_mode=True)
# cap = client.recv()
# height, width = cap.shape[:2]
while True:
# 从网络读图
frame = client.recv()
if frame is None:
break
# 解析命令行传入的参数
opt = parse_opt()
# 执行main函数
test = detectapi('runs/train/mask_new_150/weights/best.pt', frame)
# main(opt)
cv2.imshow("Output Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
client.close()
runs/train/exp4/results.csv deleted 100644 → 0
浏览文件 @ 14f614a0
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.12313, 0.058072, 0.029591, 0.0015366, 0.052817, 0.00089047, 0.00019572, 0.1218, 0.03754, 0.027899, 0.0973, 0.0003, 0.0003
1, 0.11865, 0.056151, 0.028961, 0.0021018, 0.095423, 0.0012159, 0.00031007, 0.1145, 0.039917, 0.026837, 0.093693, 0.00069307, 0.00069307
2, 0.10943, 0.055366, 0.027499, 0.0034656, 0.15176, 0.0024872, 0.00063935, 0.10284, 0.044922, 0.025336, 0.090078, 0.0010782, 0.0010782
3, 0.10193, 0.078126, 0.025495, 0.0064187, 0.34296, 0.0060875, 0.0016435, 0.093853, 0.04808, 0.023114, 0.086455, 0.0014555, 0.0014555
4, 0.095857, 0.07935, 0.022564, 0.035363, 0.16901, 0.025871, 0.007074, 0.087075, 0.048073, 0.019977, 0.082825, 0.0018248, 0.0018248
5, 0.090176, 0.082105, 0.021667, 0.60523, 0.13028, 0.074456, 0.020905, 0.083085, 0.04454, 0.017618, 0.079186, 0.0021861, 0.0021861
6, 0.082837, 0.073848, 0.020345, 0.58371, 0.12324, 0.080861, 0.026273, 0.085747, 0.049179, 0.015964, 0.07554, 0.0025396, 0.0025396
7, 0.084378, 0.069911, 0.01562, 0.59097, 0.16549, 0.074089, 0.022852, 0.082511, 0.061722, 0.015271, 0.071885, 0.0028852, 0.0028852
8, 0.077417, 0.069575, 0.017898, 0.71243, 0.19718, 0.18987, 0.063127, 0.07722, 0.056061, 0.014853, 0.068223, 0.0032228, 0.0032228
9, 0.075183, 0.069346, 0.018564, 0.71017, 0.15845, 0.19446, 0.05625, 0.077746, 0.047447, 0.014627, 0.064553, 0.0035525, 0.0035525
10, 0.072896, 0.057325, 0.018473, 0.7251, 0.26761, 0.25471, 0.093887, 0.062988, 0.045029, 0.014697, 0.060874, 0.0038743, 0.0038743
11, 0.072954, 0.057207, 0.017897, 0.61656, 0.2007, 0.14419, 0.044923, 0.073279, 0.039112, 0.014883, 0.057188, 0.0041882, 0.0041882
12, 0.073974, 0.055144, 0.018197, 0.72468, 0.27113, 0.28225, 0.085884, 0.065007, 0.036179, 0.014899, 0.053494, 0.0044941, 0.0044941
13, 0.075087, 0.064272, 0.015663, 0.68251, 0.28169, 0.24359, 0.083073, 0.066031, 0.034248, 0.014764, 0.049792, 0.0047922, 0.0047922
14, 0.069652, 0.053475, 0.015566, 0.66285, 0.26408, 0.22697, 0.074567, 0.068881, 0.034851, 0.014587, 0.046082, 0.0050823, 0.0050823
15, 0.071002, 0.05601, 0.017957, 0.18816, 0.54014, 0.231, 0.085237, 0.070401, 0.034752, 0.014523, 0.042364, 0.0053644, 0.0053644
16, 0.068644, 0.04398, 0.015856, 0.65291, 0.25595, 0.21558, 0.064298, 0.071734, 0.031342, 0.014517, 0.038639, 0.0056387, 0.0056387
17, 0.065651, 0.047164, 0.018779, 0.1922, 0.55205, 0.26347, 0.09715, 0.068808, 0.030327, 0.014502, 0.034905, 0.0059051, 0.0059051
18, 0.074219, 0.051992, 0.018119, 0.69791, 0.24296, 0.28167, 0.094196, 0.062626, 0.030746, 0.014594, 0.031163, 0.0061635, 0.0061635
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29, 0.057041, 0.048575, 0.015873, 0.29212, 0.53662, 0.3828, 0.18304, 0.061, 0.032097, 0.014304, 0.007228, 0.007228, 0.007228
30, 0.053591, 0.046568, 0.016591, 0.56371, 0.5507, 0.30788, 0.14349, 0.059073, 0.035568, 0.014401, 0.007129, 0.007129, 0.007129
31, 0.054162, 0.042137, 0.019486, 0.36389, 0.57923, 0.31064, 0.15138, 0.061972, 0.03627, 0.014697, 0.00703, 0.00703, 0.00703
32, 0.056227, 0.050044, 0.015458, 0.30077, 0.5507, 0.38941, 0.17134, 0.056464, 0.031319, 0.014676, 0.006931, 0.006931, 0.006931
33, 0.052587, 0.044581, 0.01545, 0.21514, 0.5507, 0.34583, 0.17172, 0.057614, 0.031094, 0.014847, 0.006832, 0.006832, 0.006832
34, 0.048851, 0.035901, 0.017045, 0.19523, 0.56162, 0.30665, 0.12097, 0.057432, 0.031349, 0.014902, 0.006733, 0.006733, 0.006733
35, 0.047579, 0.044077, 0.015548, 0.22087, 0.66971, 0.32553, 0.1483, 0.056925, 0.031734, 0.014673, 0.006634, 0.006634, 0.006634
36, 0.05441, 0.048046, 0.013863, 0.22974, 0.6081, 0.39427, 0.17004, 0.051781, 0.028351, 0.014835, 0.006535, 0.006535, 0.006535
37, 0.04643, 0.03983, 0.016421, 0.24045, 0.69014, 0.31944, 0.15159, 0.053751, 0.02795, 0.014697, 0.006436, 0.006436, 0.006436
38, 0.049013, 0.04616, 0.016054, 0.27497, 0.68275, 0.41693, 0.20218, 0.050051, 0.028519, 0.014423, 0.006337, 0.006337, 0.006337
39, 0.048802, 0.043735, 0.016524, 0.2914, 0.61514, 0.45342, 0.23697, 0.045618, 0.02655, 0.014304, 0.006238, 0.006238, 0.006238
40, 0.044593, 0.047139, 0.014126, 0.25618, 0.7007, 0.43711, 0.22433, 0.045531, 0.025505, 0.014233, 0.006139, 0.006139, 0.006139
41, 0.041488, 0.036973, 0.015691, 0.30722, 0.70775, 0.3773, 0.16988, 0.046599, 0.026272, 0.014169, 0.00604, 0.00604, 0.00604
42, 0.041396, 0.037737, 0.01496, 0.32366, 0.70775, 0.43703, 0.21565, 0.045712, 0.026524, 0.014304, 0.005941, 0.005941, 0.005941
43, 0.043567, 0.037117, 0.015091, 0.32407, 0.69718, 0.4362, 0.21563, 0.046044, 0.026081, 0.014438, 0.005842, 0.005842, 0.005842
44, 0.044837, 0.039713, 0.013195, 0.37023, 0.69665, 0.48075, 0.24686, 0.044902, 0.025635, 0.014252, 0.005743, 0.005743, 0.005743
45, 0.041482, 0.035317, 0.014865, 0.37933, 0.69366, 0.46705, 0.25586, 0.044474, 0.025513, 0.014301, 0.005644, 0.005644, 0.005644
46, 0.04272, 0.041213, 0.013175, 0.37614, 0.70423, 0.48301, 0.2782, 0.041666, 0.025463, 0.013913, 0.005545, 0.005545, 0.005545
47, 0.045279, 0.049565, 0.014549, 0.37521, 0.6265, 0.47197, 0.26287, 0.043755, 0.025845, 0.01395, 0.005446, 0.005446, 0.005446
48, 0.043082, 0.0434, 0.012211, 0.3993, 0.66866, 0.51135, 0.28437, 0.041001, 0.025249, 0.013794, 0.005347, 0.005347, 0.005347
49, 0.041693, 0.039008, 0.012047, 0.3834, 0.66514, 0.4985, 0.2589, 0.041728, 0.025597, 0.013617, 0.005248, 0.005248, 0.005248
50, 0.03992, 0.038466, 0.012169, 0.37221, 0.66162, 0.50167, 0.25135, 0.043418, 0.025848, 0.013376, 0.005149, 0.005149, 0.005149
51, 0.040543, 0.039796, 0.01313, 0.39426, 0.58662, 0.53292, 0.2856, 0.043435, 0.025658, 0.01333, 0.00505, 0.00505, 0.00505
52, 0.041479, 0.041072, 0.012159, 0.47167, 0.59014, 0.53926, 0.30186, 0.039959, 0.02483, 0.013388, 0.004951, 0.004951, 0.004951
53, 0.041057, 0.043748, 0.011614, 0.51221, 0.62958, 0.54876, 0.27921, 0.042747, 0.025543, 0.013361, 0.004852, 0.004852, 0.004852
54, 0.038573, 0.03799, 0.010925, 0.56291, 0.60639, 0.58807, 0.28546, 0.042119, 0.025089, 0.013043, 0.004753, 0.004753, 0.004753
55, 0.039314, 0.039958, 0.0098795, 0.52867, 0.58784, 0.57424, 0.27515, 0.043043, 0.025223, 0.012567, 0.004654, 0.004654, 0.004654
56, 0.038856, 0.039646, 0.009995, 0.55155, 0.54986, 0.59888, 0.28086, 0.042241, 0.026249, 0.012366, 0.004555, 0.004555, 0.004555
57, 0.037199, 0.034375, 0.011603, 0.57955, 0.5661, 0.59673, 0.31953, 0.039777, 0.0252, 0.012433, 0.004456, 0.004456, 0.004456
58, 0.038787, 0.041764, 0.010221, 0.57389, 0.57606, 0.59702, 0.31123, 0.04029, 0.026054, 0.012411, 0.004357, 0.004357, 0.004357
59, 0.039121, 0.048893, 0.010142, 0.50054, 0.63662, 0.59906, 0.34392, 0.037743, 0.025524, 0.012064, 0.004258, 0.004258, 0.004258
60, 0.034467, 0.035117, 0.010579, 0.54003, 0.64401, 0.62254, 0.36062, 0.038392, 0.025249, 0.011975, 0.004159, 0.004159, 0.004159
61, 0.037843, 0.040365, 0.0089292, 0.54669, 0.6831, 0.62517, 0.35155, 0.037742, 0.025009, 0.011514, 0.00406, 0.00406, 0.00406
62, 0.035774, 0.03922, 0.0090469, 0.62492, 0.67606, 0.64771, 0.38384, 0.037261, 0.025284, 0.011214, 0.003961, 0.003961, 0.003961
63, 0.036251, 0.034377, 0.0077668, 0.61886, 0.67979, 0.6781, 0.38233, 0.036565, 0.025265, 0.011046, 0.003862, 0.003862, 0.003862
64, 0.034213, 0.03489, 0.0086593, 0.64294, 0.71901, 0.68908, 0.3964, 0.036542, 0.025051, 0.010997, 0.003763, 0.003763, 0.003763
65, 0.034101, 0.032908, 0.0085104, 0.6042, 0.76901, 0.68702, 0.3812, 0.037043, 0.026482, 0.011246, 0.003664, 0.003664, 0.003664
66, 0.03509, 0.039698, 0.0083874, 0.60982, 0.74208, 0.6871, 0.39393, 0.036298, 0.025841, 0.010925, 0.003565, 0.003565, 0.003565
67, 0.033984, 0.037111, 0.007274, 0.56702, 0.76162, 0.65761, 0.38953, 0.035949, 0.025635, 0.011099, 0.003466, 0.003466, 0.003466
68, 0.033267, 0.039653, 0.0071312, 0.57576, 0.68642, 0.65153, 0.38982, 0.035677, 0.025814, 0.011111, 0.003367, 0.003367, 0.003367
69, 0.034271, 0.033655, 0.006361, 0.60213, 0.67254, 0.64857, 0.39835, 0.03528, 0.025719, 0.011363, 0.003268, 0.003268, 0.003268
70, 0.034513, 0.042021, 0.0069584, 0.55959, 0.7081, 0.6454, 0.38477, 0.03709, 0.026085, 0.0112, 0.003169, 0.003169, 0.003169
71, 0.033734, 0.041435, 0.0061397, 0.56637, 0.68659, 0.65692, 0.40445, 0.037197, 0.025948, 0.011221, 0.00307, 0.00307, 0.00307
72, 0.03348, 0.035374, 0.0059614, 0.53899, 0.74242, 0.66269, 0.41367, 0.036809, 0.025826, 0.011218, 0.002971, 0.002971, 0.002971
73, 0.030778, 0.035754, 0.0048625, 0.55255, 0.72254, 0.65435, 0.39978, 0.035515, 0.024887, 0.011337, 0.002872, 0.002872, 0.002872
74, 0.031948, 0.036283, 0.0057911, 0.57458, 0.71901, 0.67125, 0.41973, 0.035917, 0.02507, 0.011285, 0.002773, 0.002773, 0.002773
75, 0.032895, 0.035519, 0.0070188, 0.51487, 0.78034, 0.66863, 0.40384, 0.036502, 0.025219, 0.01196, 0.002674, 0.002674, 0.002674
76, 0.03486, 0.03705, 0.004849, 0.58956, 0.65141, 0.66726, 0.39786, 0.036869, 0.025646, 0.012109, 0.002575, 0.002575, 0.002575
77, 0.031633, 0.036291, 0.0049204, 0.5147, 0.8354, 0.662, 0.40421, 0.03579, 0.025616, 0.013091, 0.002476, 0.002476, 0.002476
78, 0.030949, 0.033861, 0.004955, 0.58442, 0.62289, 0.64805, 0.40307, 0.035353, 0.025345, 0.013161, 0.002377, 0.002377, 0.002377
79, 0.031921, 0.037639, 0.0046864, 0.56854, 0.64209, 0.63707, 0.40449, 0.034807, 0.02523, 0.013278, 0.002278, 0.002278, 0.002278
80, 0.030711, 0.036299, 0.0059481, 0.59359, 0.64476, 0.66648, 0.42593, 0.034985, 0.025139, 0.012503, 0.002179, 0.002179, 0.002179
81, 0.031074, 0.0317, 0.0048807, 0.62748, 0.63718, 0.69563, 0.45272, 0.03441, 0.025295, 0.012285, 0.00208, 0.00208, 0.00208
82, 0.030603, 0.031551, 0.0035231, 0.71698, 0.64472, 0.69842, 0.43535, 0.035184, 0.026154, 0.012526, 0.001981, 0.001981, 0.001981
83, 0.026633, 0.026931, 0.0030915, 0.70584, 0.64434, 0.69881, 0.44378, 0.034674, 0.026176, 0.012651, 0.001882, 0.001882, 0.001882
84, 0.028777, 0.03325, 0.0042251, 0.65659, 0.63553, 0.68895, 0.45277, 0.034298, 0.025444, 0.012224, 0.001783, 0.001783, 0.001783
85, 0.028942, 0.030564, 0.003977, 0.74662, 0.60715, 0.71278, 0.46714, 0.033988, 0.025497, 0.011702, 0.001684, 0.001684, 0.001684
86, 0.027791, 0.03487, 0.0041159, 0.83663, 0.59947, 0.71485, 0.46496, 0.033963, 0.025856, 0.01124, 0.001585, 0.001585, 0.001585
87, 0.031942, 0.039729, 0.0042078, 0.84349, 0.6072, 0.71819, 0.46045, 0.034434, 0.025688, 0.01153, 0.001486, 0.001486, 0.001486
88, 0.030888, 0.035789, 0.0039985, 0.83921, 0.61455, 0.71454, 0.45381, 0.033966, 0.025719, 0.011542, 0.001387, 0.001387, 0.001387
89, 0.027998, 0.029618, 0.00327, 0.73721, 0.65141, 0.70445, 0.46643, 0.034251, 0.025917, 0.011447, 0.001288, 0.001288, 0.001288
90, 0.034259, 0.043354, 0.0045084, 0.80912, 0.58732, 0.70422, 0.45411, 0.034134, 0.026009, 0.011539, 0.001189, 0.001189, 0.001189
91, 0.028833, 0.032577, 0.0033478, 0.80103, 0.58475, 0.68597, 0.45238, 0.033877, 0.026138, 0.011833, 0.00109, 0.00109, 0.00109
92, 0.031562, 0.047412, 0.0033903, 0.80997, 0.5838, 0.67911, 0.44635, 0.034049, 0.026184, 0.012186, 0.000991, 0.000991, 0.000991
93, 0.028363, 0.034877, 0.0027981, 0.75245, 0.5838, 0.6867, 0.44658, 0.033821, 0.026192, 0.012314, 0.000892, 0.000892, 0.000892
94, 0.028409, 0.043251, 0.0035959, 0.68365, 0.61232, 0.69079, 0.45096, 0.03401, 0.026199, 0.012492, 0.000793, 0.000793, 0.000793
95, 0.028187, 0.03397, 0.0032743, 0.70413, 0.62266, 0.68988, 0.45365, 0.03369, 0.026421, 0.012514, 0.000694, 0.000694, 0.000694
96, 0.026766, 0.031787, 0.0033969, 0.70387, 0.62935, 0.68795, 0.45498, 0.03392, 0.026306, 0.012546, 0.000595, 0.000595, 0.000595
97, 0.029564, 0.037049, 0.0036975, 0.6813, 0.6338, 0.68726, 0.45317, 0.033944, 0.026329, 0.012506, 0.000496, 0.000496, 0.000496
98, 0.025689, 0.029009, 0.0035118, 0.67363, 0.6338, 0.68812, 0.44968, 0.034085, 0.026466, 0.012349, 0.000397, 0.000397, 0.000397
99, 0.028994, 0.036311, 0.0033107, 0.67942, 0.6338, 0.6833, 0.4525, 0.034142, 0.026451, 0.012265, 0.000298, 0.000298, 0.000298
runs/train/mask_300/hyp.yaml deleted 100644 → 0
浏览文件 @ 14f614a0
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
runs/train/mask_300/opt.yaml deleted 100644 → 0
浏览文件 @ 14f614a0
weights: yolov5s.pt
cfg: owndata/yolov5s.yaml
data: owndata/data.yaml
hyp:
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
epochs: 300
batch_size: -1
imgsz: 640
rect: false
resume: false
nosave: false
noval: false
noautoanchor: false
noplots: false
evolve: null
bucket: ''
cache: null
image_weights: false
device: ''
multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 0
project: runs\train
name: exp
exist_ok: false
quad: false
cos_lr: false
label_smoothing: 0.0
patience: 100
freeze:
- 0
save_period: -1
seed: 0
local_rank: -1
entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs\train\exp3
runs/train/mask_300/results.csv deleted 100644 → 0
浏览文件 @ 14f614a0
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.12204, 0.057186, 0.029825, 0.0014397, 0.049296, 0.00083296, 0.00019082, 0.12169, 0.037575, 0.028015, 0.0973, 0.0003, 0.0003
1, 0.1201, 0.066535, 0.028965, 0.0023012, 0.10246, 0.0013475, 0.0003527, 0.11485, 0.039677, 0.02702, 0.093698, 0.00069769, 0.00069769
2, 0.11252, 0.076161, 0.027881, 0.0031055, 0.13768, 0.0021226, 0.00052123, 0.10546, 0.043266, 0.02561, 0.090093, 0.0010927, 0.0010927
3, 0.10383, 0.07396, 0.025845, 0.0053678, 0.24331, 0.0055767, 0.0015359, 0.095413, 0.046875, 0.023297, 0.086485, 0.0014851, 0.0014851
4, 0.094619, 0.080336, 0.024013, 0.0083237, 0.44155, 0.029996, 0.008294, 0.088199, 0.047234, 0.020502, 0.082875, 0.0018749, 0.0018749
5, 0.09221, 0.076818, 0.022498, 0.63506, 0.14789, 0.11806, 0.033406, 0.083354, 0.044243, 0.017584, 0.079262, 0.0022621, 0.0022621
6, 0.081383, 0.070571, 0.019174, 0.57515, 0.17958, 0.069426, 0.020135, 0.087165, 0.049438, 0.016132, 0.075647, 0.0026465, 0.0026465
7, 0.080574, 0.069388, 0.019021, 0.59364, 0.21127, 0.079938, 0.024227, 0.081107, 0.06443, 0.015485, 0.072028, 0.0030284, 0.0030284
8, 0.076023, 0.061149, 0.016966, 0.59677, 0.20775, 0.083911, 0.02164, 0.080374, 0.054192, 0.014832, 0.068408, 0.0034076, 0.0034076
9, 0.075345, 0.067929, 0.01989, 0.74324, 0.22535, 0.26675, 0.095848, 0.077161, 0.047874, 0.014832, 0.064784, 0.0037842, 0.0037842
10, 0.071691, 0.056431, 0.01919, 0.61039, 0.22535, 0.13085, 0.04382, 0.071441, 0.043015, 0.01492, 0.061158, 0.0041581, 0.0041581
11, 0.071623, 0.050783, 0.017731, 0.76814, 0.2647, 0.32065, 0.12021, 0.065883, 0.038006, 0.01506, 0.057529, 0.0045294, 0.0045294
12, 0.07304, 0.048927, 0.015598, 0.10458, 0.45845, 0.12647, 0.042271, 0.081851, 0.038513, 0.014853, 0.053898, 0.004898, 0.004898
13, 0.074987, 0.051042, 0.015413, 0.64689, 0.23592, 0.22674, 0.065214, 0.077099, 0.034817, 0.014548, 0.050264, 0.005264, 0.005264
14, 0.07505, 0.058968, 0.015631, 0.58764, 0.23944, 0.12715, 0.042205, 0.081952, 0.036057, 0.014307, 0.046627, 0.0056274, 0.0056274
15, 0.073947, 0.051407, 0.015243, 0.63738, 0.19718, 0.17001, 0.063279, 0.075434, 0.035408, 0.014249, 0.042988, 0.0059881, 0.0059881
16, 0.073384, 0.052169, 0.017095, 0.61142, 0.27817, 0.14692, 0.054067, 0.073985, 0.033165, 0.014325, 0.039346, 0.0063462, 0.0063462
17, 0.073035, 0.048784, 0.018816, 0.62151, 0.3086, 0.22067, 0.086016, 0.070285, 0.030502, 0.014273, 0.035702, 0.0067017, 0.0067017
18, 0.072267, 0.045426, 0.015777, 0.13432, 0.51866, 0.20066, 0.080139, 0.06996, 0.029594, 0.014401, 0.032054, 0.0070545, 0.0070545
19, 0.070103, 0.051928, 0.016926, 0.20428, 0.60775, 0.27001, 0.09645, 0.080033, 0.031612, 0.014328, 0.028405, 0.0074047, 0.0074047
20, 0.074408, 0.04627, 0.016107, 0.24839, 0.51162, 0.26345, 0.095021, 0.070463, 0.027962, 0.014478, 0.024752, 0.0077522, 0.0077522
21, 0.074504, 0.041126, 0.015442, 0.67316, 0.26056, 0.23931, 0.084814, 0.06849, 0.028633, 0.014569, 0.021097, 0.0080971, 0.0080971
22, 0.069236, 0.048224, 0.016952, 0.62112, 0.2007, 0.16582, 0.044676, 0.083612, 0.033943, 0.014221, 0.017439, 0.0084393, 0.0084393
23, 0.070144, 0.045525, 0.017049, 0.38048, 0.52735, 0.24669, 0.078906, 0.075391, 0.033535, 0.014224, 0.013779, 0.008779, 0.008779
24, 0.066965, 0.039295, 0.015061, 0.21936, 0.5257, 0.24792, 0.079085, 0.072672, 0.034943, 0.014117, 0.010116, 0.0091159, 0.0091159
25, 0.06661, 0.042605, 0.017057, 0.69698, 0.25352, 0.28091, 0.10082, 0.063906, 0.029507, 0.014163, 0.009175, 0.009175, 0.009175
26, 0.062617, 0.037345, 0.015638, 0.71391, 0.2993, 0.29888, 0.10163, 0.060812, 0.030949, 0.014182, 0.009175, 0.009175, 0.009175
27, 0.060336, 0.057256, 0.015888, 0.17932, 0.61479, 0.34589, 0.13563, 0.053127, 0.036346, 0.01394, 0.009142, 0.009142, 0.009142
28, 0.055362, 0.051803, 0.016164, 0.78039, 0.3169, 0.41766, 0.16224, 0.058478, 0.039333, 0.014276, 0.009109, 0.009109, 0.009109
29, 0.061177, 0.044081, 0.014042, 0.34958, 0.50106, 0.37227, 0.15298, 0.056482, 0.03326, 0.014288, 0.009076, 0.009076, 0.009076
30, 0.057322, 0.042771, 0.016789, 0.27399, 0.55845, 0.4324, 0.16419, 0.053757, 0.031578, 0.014221, 0.009043, 0.009043, 0.009043
31, 0.058989, 0.043893, 0.01776, 0.22646, 0.67606, 0.3422, 0.1217, 0.060579, 0.035015, 0.01452, 0.00901, 0.00901, 0.00901
32, 0.058178, 0.047347, 0.014876, 0.28485, 0.53697, 0.41952, 0.15235, 0.052235, 0.032104, 0.014517, 0.008977, 0.008977, 0.008977
33, 0.056758, 0.038114, 0.016426, 0.24541, 0.64754, 0.3952, 0.15808, 0.053045, 0.029995, 0.014795, 0.008944, 0.008944, 0.008944
34, 0.051085, 0.045552, 0.015305, 0.27369, 0.57218, 0.39836, 0.15943, 0.054843, 0.033531, 0.014517, 0.008911, 0.008911, 0.008911
35, 0.050087, 0.040304, 0.014254, 0.26969, 0.69014, 0.41186, 0.16922, 0.054905, 0.031994, 0.014548, 0.008878, 0.008878, 0.008878
36, 0.051903, 0.045536, 0.015133, 0.28844, 0.70458, 0.43175, 0.16254, 0.050579, 0.029701, 0.014517, 0.008845, 0.008845, 0.008845
37, 0.049929, 0.042776, 0.014451, 0.2965, 0.6081, 0.42565, 0.16876, 0.052866, 0.030788, 0.014587, 0.008812, 0.008812, 0.008812
38, 0.049026, 0.040328, 0.015294, 0.31122, 0.60106, 0.4533, 0.20822, 0.047971, 0.028809, 0.014389, 0.008779, 0.008779, 0.008779
39, 0.045871, 0.039582, 0.013313, 0.3048, 0.59131, 0.41875, 0.18184, 0.048348, 0.02803, 0.014502, 0.008746, 0.008746, 0.008746
40, 0.047753, 0.044096, 0.013781, 0.34782, 0.55106, 0.46631, 0.20244, 0.04884, 0.027691, 0.014465, 0.008713, 0.008713, 0.008713
41, 0.051785, 0.045276, 0.013886, 0.32638, 0.59754, 0.44985, 0.25304, 0.043032, 0.02681, 0.014383, 0.00868, 0.00868, 0.00868
42, 0.044502, 0.036943, 0.013827, 0.34944, 0.6831, 0.48446, 0.2071, 0.046903, 0.027203, 0.014215, 0.008647, 0.008647, 0.008647
43, 0.045049, 0.043542, 0.013292, 0.38351, 0.65106, 0.50556, 0.238, 0.04655, 0.02829, 0.014029, 0.008614, 0.008614, 0.008614
44, 0.04615, 0.039877, 0.014175, 0.3912, 0.64066, 0.5041, 0.20645, 0.04585, 0.02578, 0.014334, 0.008581, 0.008581, 0.008581
45, 0.043591, 0.040978, 0.012744, 0.31178, 0.6831, 0.47854, 0.21086, 0.047744, 0.027512, 0.013669, 0.008548, 0.008548, 0.008548
46, 0.045875, 0.04576, 0.011012, 0.52383, 0.49014, 0.45872, 0.23595, 0.041499, 0.02948, 0.012521, 0.008515, 0.008515, 0.008515
47, 0.039584, 0.038517, 0.011773, 0.30647, 0.53786, 0.42958, 0.20042, 0.048273, 0.029922, 0.012723, 0.008482, 0.008482, 0.008482
48, 0.043115, 0.042188, 0.0090955, 0.57118, 0.47958, 0.42165, 0.20979, 0.04496, 0.028687, 0.012909, 0.008449, 0.008449, 0.008449
49, 0.046104, 0.037754, 0.0080962, 0.59405, 0.47964, 0.53628, 0.24235, 0.046566, 0.027317, 0.01286, 0.008416, 0.008416, 0.008416
50, 0.044882, 0.039254, 0.0089378, 0.49937, 0.52993, 0.55766, 0.29705, 0.040842, 0.026695, 0.012503, 0.008383, 0.008383, 0.008383
51, 0.045479, 0.041197, 0.0084555, 0.48484, 0.57958, 0.55845, 0.26793, 0.042926, 0.026173, 0.012677, 0.00835, 0.00835, 0.00835
52, 0.041813, 0.035117, 0.0088189, 0.58139, 0.54085, 0.54928, 0.25112, 0.046834, 0.026958, 0.012909, 0.008317, 0.008317, 0.008317
53, 0.03867, 0.033213, 0.0073692, 0.52089, 0.56937, 0.55538, 0.27264, 0.046842, 0.026176, 0.012488, 0.008284, 0.008284, 0.008284
54, 0.043767, 0.048497, 0.0082352, 0.45368, 0.64014, 0.58178, 0.32591, 0.038962, 0.02639, 0.011578, 0.008251, 0.008251, 0.008251
55, 0.042485, 0.040842, 0.0071043, 0.56178, 0.59049, 0.61626, 0.31382, 0.041301, 0.025219, 0.011227, 0.008218, 0.008218, 0.008218
56, 0.042924, 0.044492, 0.0075323, 0.61811, 0.58345, 0.63588, 0.30172, 0.041946, 0.025806, 0.010927, 0.008185, 0.008185, 0.008185
57, 0.041339, 0.040097, 0.006421, 0.57028, 0.57641, 0.59289, 0.26238, 0.04447, 0.026237, 0.011951, 0.008152, 0.008152, 0.008152
58, 0.043951, 0.046518, 0.0071503, 0.68428, 0.52324, 0.59234, 0.2936, 0.043489, 0.026993, 0.01312, 0.008119, 0.008119, 0.008119
59, 0.043168, 0.04241, 0.0063539, 0.59511, 0.63735, 0.62153, 0.29336, 0.041413, 0.024982, 0.013126, 0.008086, 0.008086, 0.008086
60, 0.040193, 0.041138, 0.0063092, 0.55276, 0.67646, 0.6577, 0.34122, 0.042252, 0.025818, 0.0119, 0.008053, 0.008053, 0.008053
61, 0.040113, 0.039468, 0.005451, 0.58666, 0.75493, 0.66983, 0.34191, 0.041276, 0.025627, 0.011784, 0.00802, 0.00802, 0.00802
62, 0.037281, 0.030856, 0.0046724, 0.86105, 0.61658, 0.74244, 0.38608, 0.042374, 0.026802, 0.01125, 0.007987, 0.007987, 0.007987
63, 0.039282, 0.036796, 0.005337, 0.7473, 0.61937, 0.66713, 0.31924, 0.043221, 0.026909, 0.01245, 0.007954, 0.007954, 0.007954
64, 0.040247, 0.033975, 0.0046925, 0.76374, 0.60528, 0.69592, 0.31816, 0.043728, 0.027405, 0.013777, 0.007921, 0.007921, 0.007921
65, 0.038708, 0.039013, 0.0058023, 0.56708, 0.63732, 0.64766, 0.34464, 0.040524, 0.02673, 0.014938, 0.007888, 0.007888, 0.007888
66, 0.040393, 0.036453, 0.0047518, 0.55888, 0.64103, 0.63602, 0.30757, 0.043796, 0.026817, 0.015381, 0.007855, 0.007855, 0.007855
67, 0.039719, 0.041647, 0.0057742, 0.66127, 0.60588, 0.64569, 0.29617, 0.042248, 0.027191, 0.014493, 0.007822, 0.007822, 0.007822
68, 0.037532, 0.034603, 0.003788, 0.67601, 0.60141, 0.62958, 0.35141, 0.040385, 0.026081, 0.01339, 0.007789, 0.007789, 0.007789
69, 0.037489, 0.034629, 0.0045311, 0.60528, 0.63151, 0.62762, 0.34921, 0.039272, 0.027168, 0.014285, 0.007756, 0.007756, 0.007756
70, 0.038732, 0.034944, 0.0042858, 0.76107, 0.63821, 0.66592, 0.37022, 0.039586, 0.026756, 0.013553, 0.007723, 0.007723, 0.007723
71, 0.036282, 0.035574, 0.0041828, 0.84025, 0.6162, 0.68729, 0.39286, 0.03944, 0.026665, 0.013983, 0.00769, 0.00769, 0.00769
72, 0.03952, 0.035988, 0.004534, 0.79198, 0.61177, 0.66933, 0.37772, 0.039579, 0.027252, 0.012752, 0.007657, 0.007657, 0.007657
73, 0.038815, 0.042914, 0.0057031, 0.77598, 0.61972, 0.69632, 0.36799, 0.037649, 0.02602, 0.014079, 0.007624, 0.007624, 0.007624
74, 0.041208, 0.039967, 0.0053455, 0.76008, 0.59437, 0.65671, 0.36351, 0.036495, 0.026894, 0.013846, 0.007591, 0.007591, 0.007591
75, 0.03982, 0.035244, 0.0040053, 0.74174, 0.63688, 0.70955, 0.40033, 0.038208, 0.026955, 0.012875, 0.007558, 0.007558, 0.007558
76, 0.03581, 0.033562, 0.0043885, 0.87656, 0.70915, 0.78946, 0.41581, 0.038582, 0.028191, 0.012227, 0.007525, 0.007525, 0.007525
77, 0.033882, 0.030151, 0.0032131, 0.82218, 0.7088, 0.78997, 0.3929, 0.039772, 0.02816, 0.010117, 0.007492, 0.007492, 0.007492
78, 0.039961, 0.039492, 0.0041455, 0.72738, 0.72983, 0.75875, 0.42744, 0.03721, 0.026989, 0.01076, 0.007459, 0.007459, 0.007459
79, 0.038191, 0.042505, 0.003995, 0.66596, 0.64085, 0.68951, 0.43113, 0.036253, 0.026554, 0.01235, 0.007426, 0.007426, 0.007426
80, 0.037644, 0.041172, 0.0038003, 0.72356, 0.68732, 0.72551, 0.41719, 0.037449, 0.026741, 0.011771, 0.007393, 0.007393, 0.007393
81, 0.036253, 0.03502, 0.0032775, 0.79803, 0.5588, 0.67342, 0.40551, 0.037067, 0.026733, 0.013498, 0.00736, 0.00736, 0.00736
82, 0.035366, 0.035943, 0.0031932, 0.62725, 0.6017, 0.62159, 0.38154, 0.03774, 0.026859, 0.014244, 0.007327, 0.007327, 0.007327
83, 0.035218, 0.035905, 0.0028939, 0.55191, 0.66937, 0.61204, 0.36273, 0.036202, 0.025906, 0.014896, 0.007294, 0.007294, 0.007294
84, 0.035493, 0.038328, 0.0033723, 0.63892, 0.61585, 0.58303, 0.35603, 0.037468, 0.027264, 0.015186, 0.007261, 0.007261, 0.007261
85, 0.035417, 0.033739, 0.0029441, 0.78965, 0.59764, 0.73072, 0.43328, 0.039881, 0.027424, 0.013182, 0.007228, 0.007228, 0.007228
86, 0.03588, 0.037392, 0.003159, 0.65867, 0.74085, 0.74311, 0.4359, 0.03745, 0.026768, 0.012946, 0.007195, 0.007195, 0.007195
87, 0.036155, 0.032564, 0.0025667, 0.69606, 0.73476, 0.74479, 0.4292, 0.03732, 0.026318, 0.012373, 0.007162, 0.007162, 0.007162
88, 0.036036, 0.037542, 0.003245, 0.8718, 0.63089, 0.79319, 0.45559, 0.035874, 0.026043, 0.014088, 0.007129, 0.007129, 0.007129
89, 0.033554, 0.032002, 0.0029695, 0.80926, 0.7338, 0.79331, 0.48116, 0.0352, 0.025536, 0.013086, 0.007096, 0.007096, 0.007096
90, 0.03569, 0.038754, 0.0031295, 0.76497, 0.71937, 0.77379, 0.48729, 0.034915, 0.02573, 0.012419, 0.007063, 0.007063, 0.007063
91, 0.033289, 0.042538, 0.0024921, 0.70581, 0.69437, 0.72105, 0.44257, 0.035705, 0.025852, 0.012683, 0.00703, 0.00703, 0.00703
92, 0.031427, 0.030775, 0.002586, 0.67793, 0.66459, 0.70051, 0.39528, 0.038964, 0.028297, 0.013339, 0.006997, 0.006997, 0.006997
93, 0.035388, 0.040464, 0.0028139, 0.80673, 0.61585, 0.70913, 0.39168, 0.039559, 0.028786, 0.013876, 0.006964, 0.006964, 0.006964
94, 0.034815, 0.034727, 0.0027105, 0.84963, 0.69307, 0.74428, 0.43565, 0.037671, 0.028534, 0.013722, 0.006931, 0.006931, 0.006931
95, 0.033412, 0.033263, 0.0033494, 0.82712, 0.62676, 0.75783, 0.45475, 0.036206, 0.027214, 0.012962, 0.006898, 0.006898, 0.006898
96, 0.037331, 0.042266, 0.0028605, 0.78544, 0.56868, 0.6891, 0.42975, 0.034722, 0.026489, 0.013153, 0.006865, 0.006865, 0.006865
97, 0.02993, 0.032302, 0.0025345, 0.72857, 0.59437, 0.63566, 0.37941, 0.037728, 0.027134, 0.013162, 0.006832, 0.006832, 0.006832
98, 0.034297, 0.039866, 0.0040586, 0.73237, 0.65439, 0.70731, 0.43621, 0.034432, 0.026558, 0.012137, 0.006799, 0.006799, 0.006799
99, 0.035597, 0.036881, 0.0033088, 0.90954, 0.63342, 0.75369, 0.43845, 0.037873, 0.028694, 0.011638, 0.006766, 0.006766, 0.006766
100, 0.032228, 0.03512, 0.0023033, 0.79696, 0.66585, 0.73637, 0.43652, 0.03647, 0.02874, 0.010889, 0.006733, 0.006733, 0.006733
101, 0.032747, 0.032879, 0.0025279, 0.7581, 0.69437, 0.74309, 0.4444, 0.035892, 0.028503, 0.011273, 0.0067, 0.0067, 0.0067
102, 0.03237, 0.032213, 0.002525, 0.77544, 0.645, 0.73651, 0.41485, 0.038005, 0.028404, 0.011615, 0.006667, 0.006667, 0.006667
103, 0.034503, 0.032397, 0.0035894, 0.8403, 0.65176, 0.73475, 0.39521, 0.037963, 0.029121, 0.011484, 0.006634, 0.006634, 0.006634
104, 0.02933, 0.028553, 0.0033132, 0.77403, 0.60176, 0.69206, 0.40299, 0.038861, 0.028297, 0.012556, 0.006601, 0.006601, 0.006601
105, 0.031813, 0.032082, 0.0025153, 0.76155, 0.60609, 0.69128, 0.39262, 0.038421, 0.029449, 0.012479, 0.006568, 0.006568, 0.006568
106, 0.034348, 0.034996, 0.0026313, 0.70965, 0.68372, 0.7073, 0.42171, 0.036293, 0.028931, 0.012723, 0.006535, 0.006535, 0.006535
107, 0.029633, 0.030567, 0.0017493, 0.75992, 0.6088, 0.70614, 0.42582, 0.036955, 0.029129, 0.0129, 0.006502, 0.006502, 0.006502
108, 0.03126, 0.032072, 0.00252, 0.73966, 0.60386, 0.70116, 0.42066, 0.036153, 0.028084, 0.014941, 0.006469, 0.006469, 0.006469
109, 0.033641, 0.030209, 0.0027923, 0.81447, 0.59824, 0.69966, 0.38788, 0.037637, 0.028786, 0.015582, 0.006436, 0.006436, 0.006436
110, 0.031325, 0.036085, 0.0024124, 0.79665, 0.62594, 0.70085, 0.3964, 0.036597, 0.029442, 0.015775, 0.006403, 0.006403, 0.006403
111, 0.031295, 0.032462, 0.0020742, 0.70084, 0.64548, 0.68002, 0.41724, 0.036683, 0.028275, 0.01532, 0.00637, 0.00637, 0.00637
112, 0.033099, 0.037785, 0.0024526, 0.75622, 0.67676, 0.7317, 0.42493, 0.03632, 0.028328, 0.015015, 0.006337, 0.006337, 0.006337
113, 0.029867, 0.030631, 0.0021812, 0.83411, 0.6826, 0.75846, 0.43813, 0.035847, 0.028862, 0.014984, 0.006304, 0.006304, 0.006304
114, 0.028267, 0.030264, 0.0021828, 0.87676, 0.66213, 0.75076, 0.43164, 0.036414, 0.02948, 0.014304, 0.006271, 0.006271, 0.006271
115, 0.031018, 0.032151, 0.0023363, 0.78776, 0.67239, 0.7326, 0.44031, 0.036276, 0.030441, 0.01362, 0.006238, 0.006238, 0.006238
116, 0.028749, 0.031727, 0.0020639, 0.75092, 0.67676, 0.71506, 0.43918, 0.035772, 0.028366, 0.014543, 0.006205, 0.006205, 0.006205
117, 0.03109, 0.034631, 0.0023037, 0.81107, 0.6912, 0.76848, 0.45469, 0.035449, 0.029236, 0.015498, 0.006172, 0.006172, 0.006172
118, 0.03222, 0.032259, 0.0023166, 0.74228, 0.7162, 0.77625, 0.44537, 0.036397, 0.02813, 0.016411, 0.006139, 0.006139, 0.006139
119, 0.029292, 0.028276, 0.0027833, 0.80403, 0.63803, 0.76293, 0.45302, 0.03729, 0.029129, 0.019461, 0.006106, 0.006106, 0.006106
120, 0.029432, 0.032287, 0.0017818, 0.76142, 0.63405, 0.73372, 0.44232, 0.036693, 0.028282, 0.019583, 0.006073, 0.006073, 0.006073
121, 0.030413, 0.034182, 0.0023455, 0.76509, 0.68415, 0.73966, 0.44586, 0.036418, 0.028351, 0.018356, 0.00604, 0.00604, 0.00604
122, 0.03025, 0.030482, 0.0029766, 0.78038, 0.61219, 0.70131, 0.44814, 0.036141, 0.028168, 0.019977, 0.006007, 0.006007, 0.006007
123, 0.029583, 0.03239, 0.0018302, 0.81989, 0.59489, 0.68293, 0.41222, 0.036629, 0.028938, 0.018958, 0.005974, 0.005974, 0.005974
124, 0.032362, 0.034446, 0.0019004, 0.81874, 0.61486, 0.68177, 0.41319, 0.035565, 0.028702, 0.016795, 0.005941, 0.005941, 0.005941
125, 0.028447, 0.032549, 0.0018968, 0.83865, 0.64472, 0.70781, 0.42289, 0.035296, 0.028107, 0.014685, 0.005908, 0.005908, 0.005908
126, 0.032028, 0.033874, 0.0024646, 0.82508, 0.64115, 0.72718, 0.44149, 0.035399, 0.02832, 0.013705, 0.005875, 0.005875, 0.005875
127, 0.029321, 0.029734, 0.0019532, 0.86015, 0.62324, 0.73611, 0.44281, 0.034945, 0.028801, 0.012669, 0.005842, 0.005842, 0.005842
128, 0.029913, 0.033057, 0.0024026, 0.83338, 0.65465, 0.74839, 0.43793, 0.035425, 0.029068, 0.014723, 0.005809, 0.005809, 0.005809
129, 0.029851, 0.02933, 0.0021479, 0.74438, 0.71232, 0.76533, 0.46101, 0.035099, 0.028687, 0.014758, 0.005776, 0.005776, 0.005776
130, 0.02981, 0.039937, 0.0020686, 0.80545, 0.70528, 0.78026, 0.46234, 0.035441, 0.028923, 0.013412, 0.005743, 0.005743, 0.005743
131, 0.029132, 0.033458, 0.0017962, 0.8453, 0.6912, 0.76546, 0.44964, 0.035446, 0.027916, 0.013783, 0.00571, 0.00571, 0.00571
132, 0.029997, 0.032535, 0.002302, 0.83805, 0.69044, 0.76346, 0.4371, 0.035786, 0.027832, 0.013753, 0.005677, 0.005677, 0.005677
133, 0.02839, 0.030041, 0.0019298, 0.85932, 0.65935, 0.74322, 0.42973, 0.036122, 0.028351, 0.013667, 0.005644, 0.005644, 0.005644
134, 0.027294, 0.028049, 0.0018126, 0.84069, 0.68614, 0.75706, 0.45532, 0.035986, 0.028786, 0.012704, 0.005611, 0.005611, 0.005611
135, 0.027579, 0.029248, 0.0020443, 0.81678, 0.69789, 0.74464, 0.44715, 0.036509, 0.028877, 0.012587, 0.005578, 0.005578, 0.005578
136, 0.030064, 0.037469, 0.0019658, 0.90736, 0.67694, 0.76522, 0.46564, 0.036549, 0.029633, 0.011938, 0.005545, 0.005545, 0.005545
137, 0.028684, 0.030501, 0.0018615, 0.94048, 0.65797, 0.75705, 0.4484, 0.038422, 0.030258, 0.011781, 0.005512, 0.005512, 0.005512
138, 0.028257, 0.028628, 0.0024873, 0.89804, 0.66585, 0.7565, 0.46802, 0.036682, 0.029274, 0.0114, 0.005479, 0.005479, 0.005479
139, 0.029424, 0.028359, 0.0019193, 0.93053, 0.68785, 0.7884, 0.47218, 0.036337, 0.029083, 0.010913, 0.005446, 0.005446, 0.005446
140, 0.031417, 0.031184, 0.0017559, 0.92021, 0.68571, 0.78211, 0.45697, 0.036357, 0.028778, 0.01143, 0.005413, 0.005413, 0.005413
141, 0.02786, 0.027942, 0.0018515, 0.88841, 0.70865, 0.79549, 0.48535, 0.037188, 0.029015, 0.011923, 0.00538, 0.00538, 0.00538
142, 0.030125, 0.033187, 0.0020806, 0.85371, 0.71585, 0.78615, 0.45643, 0.037309, 0.027962, 0.01214, 0.005347, 0.005347, 0.005347
143, 0.026821, 0.028892, 0.0018599, 0.84385, 0.69789, 0.76237, 0.45167, 0.035666, 0.028122, 0.012071, 0.005314, 0.005314, 0.005314
144, 0.028616, 0.031743, 0.0019457, 0.9142, 0.71937, 0.7706, 0.45315, 0.036435, 0.029297, 0.011026, 0.005281, 0.005281, 0.005281
145, 0.026637, 0.030582, 0.0014506, 0.91411, 0.70638, 0.80429, 0.47398, 0.036641, 0.030296, 0.010452, 0.005248, 0.005248, 0.005248
146, 0.028454, 0.032888, 0.001842, 0.88057, 0.74106, 0.81846, 0.47364, 0.036697, 0.029594, 0.0095657, 0.005215, 0.005215, 0.005215
147, 0.030169, 0.038412, 0.0020952, 0.89896, 0.69085, 0.79502, 0.45018, 0.03673, 0.0299, 0.011169, 0.005182, 0.005182, 0.005182
148, 0.027421, 0.029499, 0.0014928, 0.86286, 0.71189, 0.79795, 0.4841, 0.036873, 0.029701, 0.011615, 0.005149, 0.005149, 0.005149
149, 0.025306, 0.029617, 0.0018112, 0.88121, 0.74673, 0.80834, 0.49563, 0.035618, 0.02919, 0.01217, 0.005116, 0.005116, 0.005116
150, 0.027502, 0.026283, 0.0019009, 0.89633, 0.69472, 0.82323, 0.48488, 0.035617, 0.030098, 0.013077, 0.005083, 0.005083, 0.005083
151, 0.026561, 0.027937, 0.0016504, 0.81893, 0.77901, 0.82174, 0.50136, 0.036669, 0.030106, 0.012493, 0.00505, 0.00505, 0.00505
152, 0.025884, 0.027816, 0.0017211, 0.82361, 0.74437, 0.80764, 0.47633, 0.037601, 0.03064, 0.01286, 0.005017, 0.005017, 0.005017
153, 0.02683, 0.028058, 0.001645, 0.74837, 0.75141, 0.80003, 0.48637, 0.03687, 0.030869, 0.012486, 0.004984, 0.004984, 0.004984
154, 0.026768, 0.030826, 0.0016743, 0.7309, 0.78729, 0.80057, 0.48696, 0.03716, 0.030296, 0.012611, 0.004951, 0.004951, 0.004951
155, 0.02482, 0.025454, 0.0013405, 0.88238, 0.63245, 0.76411, 0.47289, 0.036815, 0.030655, 0.012068, 0.004918, 0.004918, 0.004918
156, 0.027098, 0.031918, 0.0018478, 0.92896, 0.60912, 0.74918, 0.43351, 0.036887, 0.031174, 0.011179, 0.004885, 0.004885, 0.004885
157, 0.027171, 0.028445, 0.0018517, 0.76197, 0.66396, 0.73286, 0.46267, 0.034383, 0.029335, 0.011349, 0.004852, 0.004852, 0.004852
158, 0.02721, 0.029993, 0.0015743, 0.70103, 0.69463, 0.7513, 0.46244, 0.036353, 0.03006, 0.01093, 0.004819, 0.004819, 0.004819
159, 0.028064, 0.03379, 0.0016588, 0.76996, 0.65955, 0.71885, 0.45848, 0.035018, 0.028709, 0.012927, 0.004786, 0.004786, 0.004786
160, 0.026125, 0.032544, 0.0013745, 0.74028, 0.64401, 0.71491, 0.43778, 0.035224, 0.029274, 0.014255, 0.004753, 0.004753, 0.004753
161, 0.025991, 0.028481, 0.0017866, 0.6981, 0.64721, 0.69488, 0.42611, 0.035249, 0.029945, 0.014845, 0.00472, 0.00472, 0.00472
162, 0.026448, 0.033116, 0.0015659, 0.7011, 0.70528, 0.72738, 0.43289, 0.035925, 0.031143, 0.013951, 0.004687, 0.004687, 0.004687
163, 0.027095, 0.028189, 0.0020072, 0.82825, 0.66862, 0.78861, 0.49002, 0.036016, 0.031517, 0.01404, 0.004654, 0.004654, 0.004654
164, 0.026742, 0.029371, 0.0015114, 0.90087, 0.64342, 0.78638, 0.45066, 0.036238, 0.031441, 0.013966, 0.004621, 0.004621, 0.004621
165, 0.024202, 0.029219, 0.0016413, 0.73664, 0.65814, 0.73419, 0.45445, 0.035417, 0.030487, 0.01525, 0.004588, 0.004588, 0.004588
166, 0.024145, 0.029591, 0.0019572, 0.76741, 0.61545, 0.73206, 0.46378, 0.034924, 0.02961, 0.017058, 0.004555, 0.004555, 0.004555
167, 0.02477, 0.029483, 0.0018716, 0.72734, 0.71179, 0.74637, 0.45668, 0.036341, 0.030045, 0.01682, 0.004522, 0.004522, 0.004522
168, 0.026529, 0.02868, 0.0015016, 0.81464, 0.6412, 0.74898, 0.45155, 0.035556, 0.029625, 0.015837, 0.004489, 0.004489, 0.004489
169, 0.025884, 0.030197, 0.0013511, 0.85955, 0.63768, 0.77417, 0.4867, 0.035226, 0.029198, 0.014359, 0.004456, 0.004456, 0.004456
170, 0.026776, 0.028192, 0.0015519, 0.81891, 0.68315, 0.78846, 0.47858, 0.035759, 0.029633, 0.012056, 0.004423, 0.004423, 0.004423
171, 0.028303, 0.035344, 0.0015063, 0.90914, 0.68662, 0.80665, 0.48501, 0.036933, 0.030273, 0.010921, 0.00439, 0.00439, 0.00439
172, 0.025491, 0.028683, 0.0014513, 0.91124, 0.6838, 0.79552, 0.48433, 0.037365, 0.030807, 0.010829, 0.004357, 0.004357, 0.004357
173, 0.026166, 0.025964, 0.0015339, 0.79743, 0.66585, 0.76322, 0.47573, 0.035664, 0.02935, 0.011908, 0.004324, 0.004324, 0.004324
174, 0.029718, 0.032868, 0.0012887, 0.75296, 0.67567, 0.7568, 0.4698, 0.035399, 0.029167, 0.012376, 0.004291, 0.004291, 0.004291
175, 0.024449, 0.026909, 0.0016919, 0.84512, 0.68237, 0.7876, 0.49846, 0.034735, 0.028679, 0.01273, 0.004258, 0.004258, 0.004258
176, 0.023838, 0.025457, 0.0013756, 0.85405, 0.69085, 0.81535, 0.49652, 0.035796, 0.02932, 0.013229, 0.004225, 0.004225, 0.004225
177, 0.023207, 0.02692, 0.002222, 0.8423, 0.68732, 0.8128, 0.4978, 0.03595, 0.029907, 0.013461, 0.004192, 0.004192, 0.004192
178, 0.023281, 0.02767, 0.0013845, 0.84234, 0.68732, 0.80858, 0.49173, 0.035373, 0.030304, 0.012239, 0.004159, 0.004159, 0.004159
179, 0.024794, 0.030012, 0.0013742, 0.83935, 0.69085, 0.80934, 0.49404, 0.035481, 0.029625, 0.010941, 0.004126, 0.004126, 0.004126
180, 0.023126, 0.02537, 0.0013493, 0.77721, 0.72289, 0.77972, 0.49723, 0.034881, 0.029686, 0.010332, 0.004093, 0.004093, 0.004093
181, 0.024508, 0.031384, 0.0012345, 0.73301, 0.75458, 0.76232, 0.49562, 0.034467, 0.029793, 0.0098083, 0.00406, 0.00406, 0.00406
182, 0.026185, 0.031533, 0.0013347, 0.76955, 0.71651, 0.75619, 0.47542, 0.035026, 0.029694, 0.010448, 0.004027, 0.004027, 0.004027
183, 0.024714, 0.02645, 0.0016024, 0.76569, 0.71549, 0.75308, 0.46941, 0.035312, 0.029701, 0.01033, 0.003994, 0.003994, 0.003994
184, 0.023202, 0.026558, 0.0011983, 0.77852, 0.7342, 0.75581, 0.46313, 0.035528, 0.030083, 0.010561, 0.003961, 0.003961, 0.003961
185, 0.023987, 0.027324, 0.0010251, 0.78339, 0.72732, 0.76042, 0.47346, 0.035815, 0.030052, 0.011035, 0.003928, 0.003928, 0.003928
186, 0.022768, 0.02304, 0.0010367, 0.75662, 0.73345, 0.74561, 0.46598, 0.036201, 0.030746, 0.011505, 0.003895, 0.003895, 0.003895
187, 0.022989, 0.027464, 0.0011102, 0.8051, 0.71557, 0.76728, 0.47624, 0.035396, 0.030525, 0.012032, 0.003862, 0.003862, 0.003862
188, 0.024503, 0.025576, 0.002259, 0.82606, 0.70388, 0.77939, 0.4855, 0.035254, 0.031097, 0.012335, 0.003829, 0.003829, 0.003829
189, 0.024294, 0.033681, 0.0016761, 0.84932, 0.69763, 0.76569, 0.46454, 0.036434, 0.031784, 0.011639, 0.003796, 0.003796, 0.003796
190, 0.02482, 0.029747, 0.0017609, 0.78482, 0.70493, 0.76421, 0.48647, 0.035308, 0.031364, 0.011522, 0.003763, 0.003763, 0.003763
191, 0.025132, 0.03136, 0.0015368, 0.81867, 0.70012, 0.75393, 0.4777, 0.035342, 0.031624, 0.011267, 0.00373, 0.00373, 0.00373
192, 0.022949, 0.028215, 0.0011229, 0.72419, 0.70845, 0.74248, 0.47232, 0.035041, 0.031685, 0.011647, 0.003697, 0.003697, 0.003697
193, 0.022301, 0.029736, 0.0017195, 0.72022, 0.73697, 0.74856, 0.46892, 0.036089, 0.031395, 0.011969, 0.003664, 0.003664, 0.003664
194, 0.022553, 0.026508, 0.0010051, 0.81937, 0.65141, 0.7536, 0.47007, 0.036257, 0.030609, 0.011461, 0.003631, 0.003631, 0.003631
195, 0.024981, 0.028592, 0.0022498, 0.67416, 0.80458, 0.77861, 0.48094, 0.035543, 0.030449, 0.010398, 0.003598, 0.003598, 0.003598
196, 0.020531, 0.025412, 0.0011568, 0.78549, 0.75, 0.79895, 0.512, 0.035467, 0.030228, 0.0093712, 0.003565, 0.003565, 0.003565
197, 0.02367, 0.022036, 0.0014753, 0.77637, 0.75141, 0.78972, 0.50517, 0.035695, 0.030762, 0.0094063, 0.003532, 0.003532, 0.003532
198, 0.023706, 0.026919, 0.0015617, 0.77922, 0.75492, 0.80211, 0.50408, 0.036043, 0.030457, 0.010212, 0.003499, 0.003499, 0.003499
199, 0.024742, 0.029396, 0.0012192, 0.81101, 0.76784, 0.80071, 0.48403, 0.036226, 0.030983, 0.0096649, 0.003466, 0.003466, 0.003466
200, 0.022316, 0.026638, 0.0017699, 0.82426, 0.77289, 0.79913, 0.48557, 0.035834, 0.03109, 0.0095154, 0.003433, 0.003433, 0.003433
201, 0.020719, 0.025913, 0.0011327, 0.8155, 0.67289, 0.783, 0.47989, 0.035345, 0.031342, 0.010695, 0.0034, 0.0034, 0.0034
202, 0.024831, 0.030913, 0.0017394, 0.82485, 0.66937, 0.78069, 0.46408, 0.035004, 0.030586, 0.010753, 0.003367, 0.003367, 0.003367
203, 0.019763, 0.023425, 0.0010449, 0.83176, 0.66966, 0.77929, 0.47013, 0.035035, 0.030533, 0.010919, 0.003334, 0.003334, 0.003334
204, 0.022437, 0.028251, 0.0011936, 0.85243, 0.68028, 0.79402, 0.48754, 0.035377, 0.031265, 0.010794, 0.003301, 0.003301, 0.003301
205, 0.021319, 0.029154, 0.0014942, 0.84447, 0.68028, 0.78989, 0.49016, 0.035178, 0.030426, 0.011093, 0.003268, 0.003268, 0.003268
206, 0.024033, 0.025366, 0.0011678, 0.73979, 0.72242, 0.77095, 0.47406, 0.035488, 0.031128, 0.011111, 0.003235, 0.003235, 0.003235
207, 0.02225, 0.024919, 0.0012381, 0.71863, 0.72958, 0.75809, 0.46901, 0.035782, 0.031197, 0.011072, 0.003202, 0.003202, 0.003202
208, 0.022039, 0.02832, 0.0014159, 0.76766, 0.73338, 0.77884, 0.48379, 0.035574, 0.031082, 0.010025, 0.003169, 0.003169, 0.003169
209, 0.021457, 0.027852, 0.0010723, 0.75387, 0.73871, 0.78345, 0.48288, 0.035824, 0.032196, 0.0096436, 0.003136, 0.003136, 0.003136
210, 0.02402, 0.031112, 0.0015644, 0.73459, 0.76513, 0.78695, 0.49071, 0.035233, 0.031494, 0.010236, 0.003103, 0.003103, 0.003103
211, 0.024213, 0.027977, 0.0017498, 0.75612, 0.804, 0.7924, 0.49297, 0.035306, 0.031349, 0.0096077, 0.00307, 0.00307, 0.00307
212, 0.02085, 0.026565, 0.0015833, 0.73599, 0.79205, 0.79434, 0.49227, 0.035922, 0.031296, 0.010052, 0.003037, 0.003037, 0.003037
213, 0.021979, 0.027606, 0.0015668, 0.80841, 0.73732, 0.80008, 0.50453, 0.035721, 0.032166, 0.0098198, 0.003004, 0.003004, 0.003004
214, 0.022664, 0.031221, 0.001241, 0.75912, 0.71232, 0.76612, 0.47707, 0.035885, 0.032166, 0.011926, 0.002971, 0.002971, 0.002971
215, 0.021029, 0.023752, 0.0019557, 0.75663, 0.71232, 0.7784, 0.48369, 0.036312, 0.032646, 0.012555, 0.002938, 0.002938, 0.002938
216, 0.020999, 0.025235, 0.00087759, 0.78292, 0.76133, 0.80026, 0.48617, 0.036254, 0.033096, 0.011929, 0.002905, 0.002905, 0.002905
217, 0.022509, 0.026475, 0.0010609, 0.88832, 0.7338, 0.81415, 0.4875, 0.036579, 0.033005, 0.011094, 0.002872, 0.002872, 0.002872
218, 0.020774, 0.020996, 0.0010424, 0.87275, 0.76356, 0.81943, 0.49345, 0.036502, 0.032936, 0.010112, 0.002839, 0.002839, 0.002839
219, 0.023159, 0.030168, 0.001342, 0.91255, 0.76027, 0.82817, 0.49455, 0.036279, 0.033043, 0.010267, 0.002806, 0.002806, 0.002806
220, 0.021696, 0.026356, 0.001187, 0.89902, 0.71752, 0.82051, 0.48955, 0.035819, 0.032806, 0.010793, 0.002773, 0.002773, 0.002773
221, 0.019378, 0.024171, 0.0010922, 0.77162, 0.72291, 0.80347, 0.4998, 0.035449, 0.032555, 0.011494, 0.00274, 0.00274, 0.00274
222, 0.02108, 0.027847, 0.0013964, 0.73253, 0.73345, 0.78299, 0.49596, 0.035167, 0.032608, 0.012117, 0.002707, 0.002707, 0.002707
223, 0.019226, 0.025614, 0.0012317, 0.79513, 0.65325, 0.77441, 0.48983, 0.035859, 0.033195, 0.011169, 0.002674, 0.002674, 0.002674
224, 0.019902, 0.02294, 0.0012556, 0.7251, 0.74798, 0.77943, 0.48782, 0.035821, 0.0327, 0.010823, 0.002641, 0.002641, 0.002641
225, 0.022524, 0.030367, 0.0013335, 0.85533, 0.67977, 0.78949, 0.49118, 0.035572, 0.032539, 0.010712, 0.002608, 0.002608, 0.002608
226, 0.021079, 0.026147, 0.0010003, 0.72096, 0.75106, 0.76823, 0.48699, 0.034972, 0.032043, 0.011498, 0.002575, 0.002575, 0.002575
227, 0.020386, 0.026512, 0.0011049, 0.80926, 0.69049, 0.76993, 0.48767, 0.034828, 0.031914, 0.011803, 0.002542, 0.002542, 0.002542
228, 0.020455, 0.024358, 0.0011791, 0.82397, 0.67993, 0.77914, 0.48682, 0.035117, 0.032463, 0.011829, 0.002509, 0.002509, 0.002509
229, 0.021395, 0.02558, 0.00131, 0.85809, 0.6686, 0.77392, 0.4952, 0.035128, 0.032112, 0.012084, 0.002476, 0.002476, 0.002476
230, 0.018677, 0.020037, 0.0012683, 0.87727, 0.67993, 0.78097, 0.4961, 0.035656, 0.031853, 0.011362, 0.002443, 0.002443, 0.002443
231, 0.019642, 0.020411, 0.0011182, 0.76437, 0.74487, 0.78976, 0.49521, 0.035337, 0.032265, 0.01107, 0.00241, 0.00241, 0.00241
232, 0.021178, 0.026067, 0.0012819, 0.73835, 0.76971, 0.79774, 0.50557, 0.035308, 0.032257, 0.010762, 0.002377, 0.002377, 0.002377
233, 0.021115, 0.026893, 0.0010097, 0.76267, 0.76413, 0.80033, 0.49616, 0.035368, 0.032402, 0.009977, 0.002344, 0.002344, 0.002344
234, 0.022093, 0.029291, 0.0010867, 0.81323, 0.76161, 0.82136, 0.50957, 0.035088, 0.032021, 0.0088242, 0.002311, 0.002311, 0.002311
235, 0.022864, 0.037316, 0.0012292, 0.89044, 0.76071, 0.81971, 0.51074, 0.034753, 0.031746, 0.0082932, 0.002278, 0.002278, 0.002278
236, 0.022266, 0.027722, 0.001405, 0.82706, 0.75688, 0.80655, 0.51156, 0.034719, 0.031525, 0.0086655, 0.002245, 0.002245, 0.002245
237, 0.019936, 0.023236, 0.0020809, 0.88275, 0.66585, 0.78278, 0.48488, 0.034942, 0.031548, 0.0096664, 0.002212, 0.002212, 0.002212
238, 0.020529, 0.026823, 0.0012439, 0.89142, 0.67204, 0.78487, 0.49974, 0.034673, 0.031631, 0.0098946, 0.002179, 0.002179, 0.002179
239, 0.018368, 0.023137, 0.0010986, 0.78832, 0.72627, 0.77435, 0.48414, 0.035484, 0.031952, 0.0098953, 0.002146, 0.002146, 0.002146
240, 0.022334, 0.031489, 0.0012131, 0.81051, 0.70493, 0.77137, 0.47631, 0.035582, 0.032364, 0.010083, 0.002113, 0.002113, 0.002113
241, 0.01799, 0.023378, 0.0015069, 0.85126, 0.69085, 0.78083, 0.484, 0.035726, 0.032066, 0.0099396, 0.00208, 0.00208, 0.00208
242, 0.020396, 0.023032, 0.0013596, 0.87749, 0.70873, 0.80238, 0.49672, 0.035949, 0.032784, 0.0093628, 0.002047, 0.002047, 0.002047
243, 0.020887, 0.025929, 0.0011475, 0.87759, 0.71232, 0.80484, 0.51075, 0.03545, 0.032379, 0.0098679, 0.002014, 0.002014, 0.002014
244, 0.021245, 0.031842, 0.0011668, 0.84543, 0.71585, 0.80058, 0.51261, 0.035348, 0.032272, 0.010517, 0.001981, 0.001981, 0.001981
245, 0.019302, 0.023435, 0.0013087, 0.80603, 0.72173, 0.78831, 0.50335, 0.035207, 0.031921, 0.011373, 0.001948, 0.001948, 0.001948
246, 0.018977, 0.028956, 0.0012082, 0.72411, 0.73345, 0.77508, 0.49226, 0.035525, 0.032181, 0.011997, 0.001915, 0.001915, 0.001915
247, 0.020418, 0.027138, 0.001232, 0.9495, 0.61271, 0.76606, 0.48236, 0.034959, 0.032059, 0.011543, 0.001882, 0.001882, 0.001882
248, 0.019336, 0.023022, 0.00099968, 0.87626, 0.65461, 0.77604, 0.49801, 0.035327, 0.031937, 0.01075, 0.001849, 0.001849, 0.001849
249, 0.017594, 0.021221, 0.00083268, 0.8774, 0.67595, 0.78483, 0.49536, 0.035397, 0.032036, 0.010141, 0.001816, 0.001816, 0.001816
250, 0.019923, 0.025113, 0.0012777, 0.75244, 0.75914, 0.79563, 0.50284, 0.035598, 0.032654, 0.0096069, 0.001783, 0.001783, 0.001783
251, 0.018528, 0.026142, 0.0013625, 0.75119, 0.75614, 0.7861, 0.49571, 0.035447, 0.033134, 0.010361, 0.00175, 0.00175, 0.00175
252, 0.019772, 0.027159, 0.0013309, 0.81067, 0.72548, 0.78533, 0.50538, 0.035451, 0.033028, 0.010735, 0.001717, 0.001717, 0.001717
253, 0.01935, 0.026064, 0.0011216, 0.79022, 0.73111, 0.78984, 0.49942, 0.035607, 0.033058, 0.010548, 0.001684, 0.001684, 0.001684
254, 0.020353, 0.0269, 0.0009354, 0.79928, 0.74228, 0.79669, 0.50306, 0.035539, 0.033218, 0.010822, 0.001651, 0.001651, 0.001651
255, 0.017786, 0.021314, 0.00092592, 0.74746, 0.77563, 0.7895, 0.50784, 0.034853, 0.032585, 0.011414, 0.001618, 0.001618, 0.001618
256, 0.019809, 0.023237, 0.00092035, 0.72641, 0.78551, 0.78474, 0.50206, 0.034675, 0.032051, 0.011356, 0.001585, 0.001585, 0.001585
257, 0.018147, 0.021875, 0.00094907, 0.72129, 0.75845, 0.77961, 0.50468, 0.034581, 0.031914, 0.011829, 0.001552, 0.001552, 0.001552
258, 0.020004, 0.027482, 0.0016054, 0.74763, 0.76549, 0.78137, 0.50056, 0.034567, 0.031815, 0.011746, 0.001519, 0.001519, 0.001519
259, 0.018622, 0.023657, 0.00091225, 0.73771, 0.76901, 0.7756, 0.49288, 0.034612, 0.031876, 0.012003, 0.001486, 0.001486, 0.001486
260, 0.020311, 0.025651, 0.0012193, 0.71474, 0.77251, 0.77108, 0.48496, 0.034316, 0.031792, 0.012042, 0.001453, 0.001453, 0.001453
261, 0.017889, 0.020872, 0.00093694, 0.7257, 0.75587, 0.76278, 0.48169, 0.0344, 0.031837, 0.012236, 0.00142, 0.00142, 0.00142
262, 0.017891, 0.020226, 0.00088442, 0.67452, 0.70458, 0.74288, 0.46711, 0.034453, 0.031471, 0.012752, 0.001387, 0.001387, 0.001387
263, 0.017912, 0.025715, 0.00063987, 0.66385, 0.67606, 0.72524, 0.46024, 0.034404, 0.031448, 0.01331, 0.001354, 0.001354, 0.001354
264, 0.017833, 0.02198, 0.0013216, 0.67795, 0.70106, 0.73375, 0.46979, 0.034572, 0.031487, 0.013057, 0.001321, 0.001321, 0.001321
265, 0.019752, 0.026043, 0.00096803, 0.67779, 0.68164, 0.73424, 0.46827, 0.034602, 0.031494, 0.013147, 0.001288, 0.001288, 0.001288
266, 0.018818, 0.025843, 0.0012794, 0.69525, 0.74401, 0.76407, 0.49017, 0.034693, 0.03154, 0.012297, 0.001255, 0.001255, 0.001255
267, 0.017994, 0.023989, 0.00090663, 0.73786, 0.76415, 0.77806, 0.49632, 0.035052, 0.032082, 0.011598, 0.001222, 0.001222, 0.001222
268, 0.02022, 0.026347, 0.0011837, 0.8013, 0.70493, 0.77479, 0.49209, 0.034923, 0.031822, 0.011694, 0.001189, 0.001189, 0.001189
269, 0.019225, 0.026445, 0.0010087, 0.79911, 0.70493, 0.78187, 0.50009, 0.035071, 0.032166, 0.011793, 0.001156, 0.001156, 0.001156
270, 0.018599, 0.025434, 0.00082728, 0.77222, 0.7297, 0.78367, 0.50286, 0.03516, 0.032616, 0.01172, 0.001123, 0.001123, 0.001123
271, 0.017672, 0.023844, 0.00085246, 0.82176, 0.69354, 0.79272, 0.51152, 0.035261, 0.032951, 0.011209, 0.00109, 0.00109, 0.00109
272, 0.018049, 0.022264, 0.00094761, 0.7522, 0.78345, 0.79788, 0.5132, 0.034965, 0.03257, 0.011251, 0.001057, 0.001057, 0.001057
273, 0.016614, 0.02285, 0.0008537, 0.76975, 0.77492, 0.79419, 0.51977, 0.034746, 0.032333, 0.01136, 0.001024, 0.001024, 0.001024
274, 0.018981, 0.026508, 0.00094938, 0.77745, 0.75845, 0.7964, 0.50974, 0.035151, 0.032661, 0.011176, 0.000991, 0.000991, 0.000991
275, 0.017882, 0.020587, 0.00090807, 0.74135, 0.77511, 0.79668, 0.51038, 0.035084, 0.032661, 0.010878, 0.000958, 0.000958, 0.000958
276, 0.015894, 0.020184, 0.00050942, 0.74924, 0.77492, 0.79837, 0.50875, 0.035251, 0.032753, 0.010696, 0.000925, 0.000925, 0.000925
277, 0.017934, 0.023442, 0.00092812, 0.78237, 0.75141, 0.80047, 0.51153, 0.035157, 0.032806, 0.01049, 0.000892, 0.000892, 0.000892
278, 0.02063, 0.029007, 0.0010539, 0.7844, 0.75141, 0.80348, 0.50997, 0.035501, 0.03344, 0.010293, 0.000859, 0.000859, 0.000859
279, 0.016085, 0.019594, 0.00074056, 0.85215, 0.69903, 0.80357, 0.50866, 0.035349, 0.033684, 0.010319, 0.000826, 0.000826, 0.000826
280, 0.016963, 0.02141, 0.0008783, 0.7815, 0.7384, 0.80026, 0.50511, 0.035237, 0.033607, 0.010311, 0.000793, 0.000793, 0.000793
281, 0.016612, 0.021743, 0.000581, 0.73749, 0.75647, 0.79397, 0.50902, 0.035178, 0.033485, 0.010577, 0.00076, 0.00076, 0.00076
282, 0.018598, 0.022024, 0.00072089, 0.75452, 0.77762, 0.797, 0.50176, 0.035348, 0.03373, 0.010735, 0.000727, 0.000727, 0.000727
283, 0.016935, 0.02254, 0.0011607, 0.73972, 0.74836, 0.78464, 0.50511, 0.035208, 0.033127, 0.01132, 0.000694, 0.000694, 0.000694
284, 0.01724, 0.022562, 0.00083746, 0.74304, 0.74944, 0.78479, 0.50236, 0.035192, 0.032768, 0.011766, 0.000661, 0.000661, 0.000661
285, 0.016465, 0.020218, 0.00083152, 0.74816, 0.75133, 0.77271, 0.49387, 0.035047, 0.032425, 0.012369, 0.000628, 0.000628, 0.000628
286, 0.015606, 0.020532, 0.00074262, 0.73311, 0.73523, 0.76965, 0.49686, 0.034959, 0.032272, 0.012802, 0.000595, 0.000595, 0.000595
287, 0.01779, 0.028304, 0.00076472, 0.7396, 0.73059, 0.76845, 0.48852, 0.035254, 0.032478, 0.013078, 0.000562, 0.000562, 0.000562
288, 0.016072, 0.020904, 0.0011696, 0.73643, 0.72996, 0.7614, 0.48601, 0.035227, 0.0326, 0.013251, 0.000529, 0.000529, 0.000529
289, 0.016642, 0.019281, 0.00048293, 0.72101, 0.71549, 0.75987, 0.48649, 0.035221, 0.032829, 0.013361, 0.000496, 0.000496, 0.000496
290, 0.017662, 0.022636, 0.00066127, 0.71409, 0.71667, 0.76195, 0.48813, 0.035083, 0.032845, 0.013361, 0.000463, 0.000463, 0.000463
291, 0.015376, 0.020929, 0.00075289, 0.71988, 0.71197, 0.76148, 0.49366, 0.034872, 0.032623, 0.013292, 0.00043, 0.00043, 0.00043
292, 0.017058, 0.022933, 0.00077311, 0.72066, 0.71197, 0.76457, 0.49837, 0.034941, 0.032661, 0.013103, 0.000397, 0.000397, 0.000397
293, 0.01659, 0.022511, 0.00078295, 0.72629, 0.71197, 0.76298, 0.49522, 0.034978, 0.032852, 0.013216, 0.000364, 0.000364, 0.000364
294, 0.018664, 0.02643, 0.00086355, 0.73126, 0.68641, 0.7633, 0.49756, 0.034895, 0.032845, 0.01331, 0.000331, 0.000331, 0.000331
295, 0.018675, 0.030918, 0.00069338, 0.73117, 0.68151, 0.76461, 0.49374, 0.034915, 0.032921, 0.013406, 0.000298, 0.000298, 0.000298
296, 0.015279, 0.02058, 0.0007038, 0.73151, 0.68581, 0.76679, 0.49607, 0.034866, 0.032867, 0.013354, 0.000265, 0.000265, 0.000265
297, 0.017575, 0.025418, 0.00098743, 0.69133, 0.73641, 0.76632, 0.49777, 0.034821, 0.032761, 0.013412, 0.000232, 0.000232, 0.000232
298, 0.01577, 0.022512, 0.00077127, 0.67221, 0.74011, 0.76528, 0.49937, 0.034745, 0.032631, 0.01335, 0.000199, 0.000199, 0.000199
299, 0.01868, 0.020464, 0.00099828, 0.69968, 0.74464, 0.77029, 0.50272, 0.03476, 0.032669, 0.013263, 0.000166, 0.000166, 0.000166
runs/train/mask_new_150/hyp.yaml deleted 100644 → 0
浏览文件 @ 14f614a0
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
runs/train/mask_new_300/hyp.yaml deleted 100644 → 0
浏览文件 @ 14f614a0
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 0.05
cls: 0.5
cls_pw: 1.0
obj: 1.0
obj_pw: 1.0
iou_t: 0.2
anchor_t: 4.0
fl_gamma: 0.0
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
train.py
浏览文件 @ 34d6c085
......@@ -464,7 +464,7 @@ def parse_opt(known=False):
parser.add_argument('--cfg', type=str, default='owndata/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='owndata/mask.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
parser.add_argument('--batch-size', type=int, default=-1, help='total batch size for all GPUs, -1 for autobatch')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
parser.add_argument('--rect', action='store_true', help='rectangular training')
......
runs/train/exp4/opt.yaml → weights/mask_full/opt.yaml
浏览文件 @ 34d6c085
weights: yolov5s.pt
cfg: owndata/yolov5s.yaml
data: owndata/data.yaml
data: owndata/mask.yaml
hyp:
lr0: 0.01
lrf: 0.01
......@@ -31,7 +31,7 @@ hyp:
mixup: 0.0
copy_paste: 0.0
epochs: 100
batch_size: 32
batch_size: -1
imgsz: 640
rect: false
resume: false
......@@ -48,7 +48,7 @@ multi_scale: false
single_cls: false
optimizer: SGD
sync_bn: false
workers: 8
workers: 0
project: runs/train
name: exp
exist_ok: false
......@@ -65,4 +65,4 @@ entity: null
upload_dataset: false
bbox_interval: -1
artifact_alias: latest
save_dir: runs/train/exp4
save_dir: runs/train/exp9
weights/mask_full/results.csv 0 → 100644
浏览文件 @ 34d6c085
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.074287, 0.030195, 0.01819, 0.27499, 0.18294, 0.21771, 0.080169, 0.06371, 0.024313, 0.005793, 0.070169, 0.0033145, 0.0033145
1, 0.054021, 0.020867, 0.0076381, 0.4782, 0.41078, 0.36837, 0.17925, 0.053154, 0.022344, 0.0052433, 0.040104, 0.006582, 0.006582
2, 0.045046, 0.018329, 0.0056352, 0.33889, 0.43683, 0.38067, 0.17938, 0.044381, 0.022826, 0.0031698, 0.0099719, 0.0097835, 0.0097835
3, 0.038505, 0.018142, 0.0052275, 0.54763, 0.41733, 0.51089, 0.24868, 0.03951, 0.021213, 0.0017176, 0.009703, 0.009703, 0.009703
4, 0.035638, 0.017766, 0.0045196, 0.50216, 0.6035, 0.4701, 0.24282, 0.040762, 0.021665, 0.0014984, 0.009703, 0.009703, 0.009703
5, 0.033779, 0.017321, 0.0041134, 0.63152, 0.71453, 0.69134, 0.38037, 0.037319, 0.019548, 0.00096574, 0.009604, 0.009604, 0.009604
6, 0.032456, 0.016603, 0.0039885, 0.75246, 0.72838, 0.74537, 0.38688, 0.036203, 0.019639, 0.0004992, 0.009505, 0.009505, 0.009505
7, 0.032227, 0.0165, 0.0038135, 0.71536, 0.55588, 0.60448, 0.31967, 0.036851, 0.019705, 0.00086956, 0.009406, 0.009406, 0.009406
8, 0.0315, 0.016129, 0.0034152, 0.67834, 0.82394, 0.77898, 0.39325, 0.034316, 0.019177, 0.00068259, 0.009307, 0.009307, 0.009307
9, 0.031633, 0.015973, 0.003446, 0.72, 0.79659, 0.71963, 0.39204, 0.034118, 0.018756, 0.00070906, 0.009208, 0.009208, 0.009208
10, 0.030703, 0.015969, 0.0033888, 0.95008, 0.70913, 0.7666, 0.46109, 0.033029, 0.018597, 0.00044274, 0.009109, 0.009109, 0.009109
11, 0.0301, 0.01579, 0.0033416, 0.7775, 0.84524, 0.84041, 0.46685, 0.032794, 0.018079, 0.00048411, 0.00901, 0.00901, 0.00901
12, 0.030077, 0.015911, 0.0030406, 0.90572, 0.91684, 0.93139, 0.49067, 0.031946, 0.018094, 0.00040147, 0.008911, 0.008911, 0.008911
13, 0.029715, 0.015524, 0.0029878, 0.85577, 0.70287, 0.82053, 0.43894, 0.032027, 0.017882, 0.00059018, 0.008812, 0.008812, 0.008812
14, 0.029376, 0.015516, 0.0028926, 0.87056, 0.80422, 0.88951, 0.47734, 0.031894, 0.017545, 0.00049963, 0.008713, 0.008713, 0.008713
15, 0.029457, 0.015337, 0.0026673, 0.94901, 0.62404, 0.81067, 0.46761, 0.031396, 0.017627, 0.00032618, 0.008614, 0.008614, 0.008614
16, 0.029037, 0.015156, 0.002636, 0.82289, 0.887, 0.90969, 0.51951, 0.031889, 0.017968, 0.00061762, 0.008515, 0.008515, 0.008515
17, 0.028726, 0.015138, 0.0027539, 0.77001, 0.82701, 0.85871, 0.4959, 0.03086, 0.01734, 0.00027285, 0.008416, 0.008416, 0.008416
18, 0.028604, 0.015019, 0.0026561, 0.85077, 0.92566, 0.91554, 0.50942, 0.030917, 0.017352, 0.00022389, 0.008317, 0.008317, 0.008317
19, 0.028239, 0.015073, 0.00254, 0.93386, 0.9169, 0.94607, 0.53114, 0.030252, 0.016837, 0.00030084, 0.008218, 0.008218, 0.008218
20, 0.02816, 0.014997, 0.0023906, 0.94613, 0.73604, 0.86672, 0.49776, 0.029936, 0.016645, 0.00018438, 0.008119, 0.008119, 0.008119
21, 0.028011, 0.014664, 0.0025164, 0.89014, 0.90743, 0.94677, 0.53579, 0.030344, 0.016546, 0.00028551, 0.00802, 0.00802, 0.00802
22, 0.028167, 0.014791, 0.0024191, 0.97375, 0.88966, 0.94709, 0.54257, 0.030288, 0.016747, 0.00041261, 0.007921, 0.007921, 0.007921
23, 0.028336, 0.01481, 0.0022024, 0.8868, 0.91545, 0.91584, 0.48497, 0.030065, 0.016518, 0.00031524, 0.007822, 0.007822, 0.007822
24, 0.027482, 0.014759, 0.002284, 0.97346, 0.90767, 0.95089, 0.51518, 0.029673, 0.016615, 0.00018575, 0.007723, 0.007723, 0.007723
25, 0.027783, 0.014671, 0.0022426, 0.84103, 0.91108, 0.9298, 0.54031, 0.028781, 0.016335, 0.0002098, 0.007624, 0.007624, 0.007624
26, 0.027899, 0.014628, 0.0022248, 0.7374, 0.90962, 0.81873, 0.44229, 0.029377, 0.016283, 0.00095067, 0.007525, 0.007525, 0.007525
27, 0.027472, 0.014475, 0.0022079, 0.80708, 0.93805, 0.9174, 0.54089, 0.029381, 0.016543, 0.00028412, 0.007426, 0.007426, 0.007426
28, 0.027007, 0.014356, 0.0021174, 0.94681, 0.92292, 0.95711, 0.54043, 0.028725, 0.01619, 0.00019134, 0.007327, 0.007327, 0.007327
29, 0.026918, 0.01418, 0.0022085, 0.94691, 0.93929, 0.95369, 0.52508, 0.028548, 0.016283, 0.00016114, 0.007228, 0.007228, 0.007228
30, 0.027142, 0.014503, 0.0020554, 0.86516, 0.84141, 0.85209, 0.49628, 0.028449, 0.016178, 0.00012097, 0.007129, 0.007129, 0.007129
31, 0.027009, 0.014353, 0.001907, 0.95627, 0.90743, 0.94607, 0.54504, 0.028583, 0.015911, 0.00011259, 0.00703, 0.00703, 0.00703
32, 0.027151, 0.014551, 0.0018981, 0.95405, 0.91399, 0.95297, 0.56423, 0.028149, 0.015822, 9.976e-05, 0.006931, 0.006931, 0.006931
33, 0.026671, 0.014021, 0.0018812, 0.86388, 0.93331, 0.94214, 0.56012, 0.028234, 0.015787, 0.00014782, 0.006832, 0.006832, 0.006832
34, 0.026587, 0.014213, 0.0017784, 0.96938, 0.90192, 0.96043, 0.58954, 0.027738, 0.015618, 0.00010669, 0.006733, 0.006733, 0.006733
35, 0.026633, 0.01399, 0.0017429, 0.95505, 0.90356, 0.95161, 0.56641, 0.027867, 0.01573, 0.00012056, 0.006634, 0.006634, 0.006634
36, 0.026371, 0.013865, 0.0018075, 0.89285, 0.91144, 0.93437, 0.56927, 0.027595, 0.015802, 0.00010676, 0.006535, 0.006535, 0.006535
37, 0.02589, 0.013742, 0.0017403, 0.92966, 0.90286, 0.95896, 0.58276, 0.027393, 0.015347, 8.9526e-05, 0.006436, 0.006436, 0.006436
38, 0.025975, 0.01397, 0.0016506, 0.97385, 0.89067, 0.96007, 0.60387, 0.027232, 0.015335, 0.00010493, 0.006337, 0.006337, 0.006337
39, 0.026022, 0.013872, 0.0016615, 0.95874, 0.9143, 0.95961, 0.62618, 0.027124, 0.015151, 0.00012406, 0.006238, 0.006238, 0.006238
40, 0.025817, 0.013582, 0.0015502, 0.96416, 0.91983, 0.96172, 0.60539, 0.026635, 0.015228, 5.6273e-05, 0.006139, 0.006139, 0.006139
41, 0.026233, 0.013824, 0.0015728, 0.94658, 0.92283, 0.95951, 0.57884, 0.026434, 0.015062, 7.301e-05, 0.00604, 0.00604, 0.00604
42, 0.025741, 0.013796, 0.0014174, 0.96699, 0.93149, 0.96143, 0.56873, 0.026314, 0.014951, 5.6851e-05, 0.005941, 0.005941, 0.005941
43, 0.025908, 0.013945, 0.0015983, 0.96492, 0.91923, 0.96168, 0.57074, 0.026614, 0.015108, 9.6345e-05, 0.005842, 0.005842, 0.005842
44, 0.025751, 0.014064, 0.0014813, 0.95218, 0.92117, 0.96377, 0.59246, 0.026117, 0.015008, 9.3096e-05, 0.005743, 0.005743, 0.005743
45, 0.025318, 0.01355, 0.0014146, 0.96348, 0.92744, 0.96375, 0.58905, 0.025985, 0.014972, 5.4282e-05, 0.005644, 0.005644, 0.005644
46, 0.025501, 0.013543, 0.0014254, 0.94212, 0.94276, 0.96124, 0.60626, 0.02548, 0.014944, 6.3747e-05, 0.005545, 0.005545, 0.005545
47, 0.02516, 0.013543, 0.0015356, 0.96638, 0.92905, 0.96207, 0.60572, 0.025678, 0.014773, 6.7359e-05, 0.005446, 0.005446, 0.005446
48, 0.025372, 0.013349, 0.0013997, 0.95735, 0.9499, 0.96556, 0.61635, 0.025155, 0.01469, 6.4921e-05, 0.005347, 0.005347, 0.005347
49, 0.025265, 0.013491, 0.0014703, 0.96539, 0.94606, 0.96497, 0.60312, 0.025169, 0.014697, 5.1463e-05, 0.005248, 0.005248, 0.005248
50, 0.024931, 0.013154, 0.0014336, 0.96525, 0.93878, 0.96493, 0.60029, 0.025491, 0.014669, 5.0497e-05, 0.005149, 0.005149, 0.005149
51, 0.024683, 0.013444, 0.0013011, 0.95393, 0.93294, 0.96714, 0.60551, 0.025082, 0.014553, 5.3519e-05, 0.00505, 0.00505, 0.00505
52, 0.024937, 0.013451, 0.001267, 0.88677, 0.92566, 0.96568, 0.58848, 0.02477, 0.014337, 4.8089e-05, 0.004951, 0.004951, 0.004951
53, 0.02462, 0.013236, 0.0011537, 0.95302, 0.89723, 0.96701, 0.60684, 0.024708, 0.014222, 4.206e-05, 0.004852, 0.004852, 0.004852
54, 0.024409, 0.013166, 0.0012052, 0.95635, 0.90306, 0.96708, 0.61921, 0.024929, 0.014232, 4.8289e-05, 0.004753, 0.004753, 0.004753
55, 0.024242, 0.013061, 0.0012593, 0.92856, 0.92904, 0.96614, 0.61946, 0.024886, 0.014165, 4.805e-05, 0.004654, 0.004654, 0.004654
56, 0.024399, 0.013404, 0.0012828, 0.93732, 0.92727, 0.96823, 0.62978, 0.024508, 0.014066, 3.8484e-05, 0.004555, 0.004555, 0.004555
57, 0.023898, 0.012911, 0.0011668, 0.95651, 0.91352, 0.96699, 0.64225, 0.024282, 0.013896, 3.6728e-05, 0.004456, 0.004456, 0.004456
58, 0.023986, 0.012869, 0.0012388, 0.95371, 0.92981, 0.96898, 0.65423, 0.024156, 0.013821, 3.3817e-05, 0.004357, 0.004357, 0.004357
59, 0.023926, 0.012979, 0.0012519, 0.96182, 0.91954, 0.96762, 0.64062, 0.024061, 0.013801, 3.2902e-05, 0.004258, 0.004258, 0.004258
60, 0.023749, 0.012688, 0.0012024, 0.965, 0.92121, 0.9687, 0.62136, 0.023998, 0.013789, 3.3492e-05, 0.004159, 0.004159, 0.004159
61, 0.023495, 0.012735, 0.0011955, 0.96535, 0.92215, 0.96896, 0.61407, 0.023716, 0.013657, 3.2306e-05, 0.00406, 0.00406, 0.00406
62, 0.023772, 0.012746, 0.0010705, 0.96181, 0.93003, 0.96917, 0.61149, 0.02362, 0.013618, 3.1373e-05, 0.003961, 0.003961, 0.003961
63, 0.023313, 0.012655, 0.0011098, 0.96628, 0.92448, 0.96968, 0.62455, 0.023328, 0.01358, 3.3239e-05, 0.003862, 0.003862, 0.003862
64, 0.023322, 0.012743, 0.0010713, 0.96233, 0.9191, 0.97135, 0.62353, 0.023262, 0.013524, 3.1629e-05, 0.003763, 0.003763, 0.003763
65, 0.023228, 0.012784, 0.0010469, 0.96901, 0.92606, 0.96999, 0.61111, 0.02304, 0.013419, 3.1766e-05, 0.003664, 0.003664, 0.003664
66, 0.02335, 0.012638, 0.0010285, 0.96655, 0.93344, 0.97068, 0.62219, 0.022952, 0.013346, 2.9695e-05, 0.003565, 0.003565, 0.003565
67, 0.022916, 0.012262, 0.0010666, 0.96492, 0.93659, 0.97388, 0.62355, 0.02284, 0.01328, 2.9317e-05, 0.003466, 0.003466, 0.003466
68, 0.022995, 0.012517, 0.0011394, 0.96261, 0.93535, 0.97176, 0.63515, 0.022752, 0.013196, 2.8655e-05, 0.003367, 0.003367, 0.003367
69, 0.02248, 0.012244, 0.0010436, 0.95769, 0.93732, 0.97182, 0.64689, 0.022625, 0.013101, 2.802e-05, 0.003268, 0.003268, 0.003268
70, 0.022739, 0.012194, 0.00098815, 0.95795, 0.93705, 0.97224, 0.63179, 0.022501, 0.01301, 2.6593e-05, 0.003169, 0.003169, 0.003169
71, 0.022671, 0.012066, 0.0010002, 0.96337, 0.93931, 0.97258, 0.63414, 0.022433, 0.012956, 2.5421e-05, 0.00307, 0.00307, 0.00307
72, 0.022465, 0.012401, 0.0010157, 0.96161, 0.93801, 0.9722, 0.65341, 0.022365, 0.01295, 2.4697e-05, 0.002971, 0.002971, 0.002971
73, 0.022453, 0.012342, 0.00095603, 0.95928, 0.93732, 0.97283, 0.66249, 0.022264, 0.012924, 2.4453e-05, 0.002872, 0.002872, 0.002872
74, 0.022339, 0.012169, 0.00097452, 0.95817, 0.94192, 0.97319, 0.67227, 0.022152, 0.01288, 2.4295e-05, 0.002773, 0.002773, 0.002773
75, 0.022453, 0.012239, 0.00098431, 0.9599, 0.94155, 0.97328, 0.65985, 0.022116, 0.012872, 2.4205e-05, 0.002674, 0.002674, 0.002674
76, 0.022129, 0.012168, 0.00095869, 0.96236, 0.93659, 0.9735, 0.6807, 0.021986, 0.012825, 2.3699e-05, 0.002575, 0.002575, 0.002575
77, 0.021596, 0.011998, 0.00092008, 0.96075, 0.93771, 0.97347, 0.69103, 0.021929, 0.012795, 2.3305e-05, 0.002476, 0.002476, 0.002476
78, 0.022155, 0.01198, 0.00097452, 0.96003, 0.93873, 0.97299, 0.66977, 0.021869, 0.012768, 2.3264e-05, 0.002377, 0.002377, 0.002377
79, 0.021719, 0.011964, 0.00082631, 0.95938, 0.93786, 0.97135, 0.67303, 0.021802, 0.012732, 2.2525e-05, 0.002278, 0.002278, 0.002278
80, 0.021436, 0.011958, 0.00083157, 0.95986, 0.93878, 0.97269, 0.66743, 0.021726, 0.012691, 2.2247e-05, 0.002179, 0.002179, 0.002179
81, 0.021355, 0.011713, 0.00085203, 0.9605, 0.93858, 0.97265, 0.67795, 0.021646, 0.012662, 2.1666e-05, 0.00208, 0.00208, 0.00208
82, 0.021592, 0.011839, 0.00086464, 0.96279, 0.93734, 0.97273, 0.68474, 0.021594, 0.012649, 2.1115e-05, 0.001981, 0.001981, 0.001981
83, 0.021159, 0.011666, 0.0007883, 0.963, 0.93975, 0.97357, 0.69132, 0.021529, 0.012612, 2.0167e-05, 0.001882, 0.001882, 0.001882
84, 0.021276, 0.011958, 0.00081182, 0.96416, 0.94169, 0.97359, 0.6931, 0.021463, 0.012578, 1.9626e-05, 0.001783, 0.001783, 0.001783
85, 0.021111, 0.011558, 0.00085325, 0.96385, 0.94044, 0.97258, 0.69292, 0.021392, 0.012553, 1.9033e-05, 0.001684, 0.001684, 0.001684
86, 0.02078, 0.011453, 0.00083054, 0.96503, 0.9421, 0.97319, 0.6927, 0.021312, 0.012528, 1.8655e-05, 0.001585, 0.001585, 0.001585
87, 0.020774, 0.011607, 0.00072877, 0.96755, 0.9395, 0.97331, 0.68676, 0.02124, 0.012498, 1.8165e-05, 0.001486, 0.001486, 0.001486
88, 0.021018, 0.011631, 0.00072179, 0.96708, 0.93951, 0.97338, 0.69391, 0.021165, 0.012471, 1.7548e-05, 0.001387, 0.001387, 0.001387
89, 0.020399, 0.011452, 0.00071053, 0.96457, 0.94525, 0.97346, 0.69447, 0.021106, 0.012439, 1.6917e-05, 0.001288, 0.001288, 0.001288
90, 0.020256, 0.011265, 0.0006999, 0.96699, 0.94212, 0.97345, 0.68815, 0.021031, 0.012408, 1.6496e-05, 0.001189, 0.001189, 0.001189
91, 0.020157, 0.011239, 0.0007525, 0.9679, 0.94066, 0.97339, 0.6957, 0.020975, 0.012381, 1.5999e-05, 0.00109, 0.00109, 0.00109
92, 0.020034, 0.011416, 0.00070712, 0.96745, 0.94241, 0.97339, 0.69576, 0.020931, 0.012356, 1.5575e-05, 0.000991, 0.000991, 0.000991
93, 0.020398, 0.011321, 0.00065654, 0.96764, 0.9392, 0.9734, 0.69258, 0.020888, 0.012335, 1.5141e-05, 0.000892, 0.000892, 0.000892
94, 0.019792, 0.010941, 0.00068327, 0.96764, 0.94128, 0.97322, 0.69691, 0.020841, 0.012312, 1.476e-05, 0.000793, 0.000793, 0.000793
95, 0.019945, 0.011067, 0.00072616, 0.97292, 0.93659, 0.9746, 0.69742, 0.020793, 0.012292, 1.4392e-05, 0.000694, 0.000694, 0.000694
96, 0.019758, 0.01116, 0.00067778, 0.97353, 0.93586, 0.97463, 0.69773, 0.020742, 0.012274, 1.404e-05, 0.000595, 0.000595, 0.000595
97, 0.019735, 0.011117, 0.00063999, 0.9748, 0.93512, 0.97461, 0.69801, 0.020694, 0.012249, 1.3605e-05, 0.000496, 0.000496, 0.000496
98, 0.019582, 0.010874, 0.0007205, 0.97548, 0.93377, 0.9746, 0.69809, 0.020656, 0.012234, 1.3195e-05, 0.000397, 0.000397, 0.000397
99, 0.019534, 0.011067, 0.00065022, 0.97559, 0.9344, 0.97461, 0.69839, 0.020624, 0.012216, 1.277e-05, 0.000298, 0.000298, 0.000298
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